Disclaimer: The purpose of the Open Case Studies project is to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts.

This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 (CC BY-NC 3.0) United States License.

Motivation


This case study will introduce the topic of multicolinearity. We will do so by showcasing a real world example where multicolinearity in part resulted in historically contriversial and conflicting findings about the influence of the adoption of right-to-carry (RTC) concealed handgun laws on violent crime rates in the United States.

We will focus on two articles:

  1. The first analysis by Lott and Mustard published in 1996 suggests that RTC laws reduce violent crime. Lott authored a book extending these findings in 1998 called More Guns, Less Crime.

  1. The second analysis is a recent article by Donohue, et al. published in 2017 that suggests that RTC laws increase violent crime. Donohue has also published previous articles with titles such as “Shooting down the”More Guns, Less Crime" Hypothesis

This has been a controversial topic as many other articles also had conflicting results. See here for a list of studies.

The Donohue, et al. article discusses how there are many other important methodolical aspects besides multicolinearity that could account for the historically conflicting results in these previous papers.

In fact, nearly every aspect of the data analysis process was different between the Donohue, et al. analysis and the Lott and Mustard analysis.

However, we will focus particularly on multicolinearity and we will explore how it can influence linear regression analyses and result in different conclusions.

This analysis will demonstrate how methodological details can be critically influential for our overall conclusions and can result in important policy related consequences. This article will provide a basis for the motivation.

John J. Donohue et al., Right‐to‐Carry Laws and Violent Crime: A Comprehensive Assessment Using Panel Data and a State‐Level Synthetic Control Analysis. Journal of Empirical Legal Studies, 16,2 (2019).

David B. Mustard & John Lott. Crime, Deterrence, and Right-to-Carry Concealed Handguns. Coase-Sandor Institute for Law & Economics Working Paper No. 41, (1996).

Here you can see the differences in the data used in the featured RTC articles:

We will perform analyses similar to those in these articles, however we will not try to recreate them, instead we will simplify our analysis to allow us to focus on multicolinearity.

Therefore we will use a subset of the listed explanatory variables and they will be consistent for both analyses that we will perform, with the exception that one analysis will have 6 demographic variables like the analysis in the Donohue, et al. article and the other will have 36 demogrpahic variables like the analysis in the Lott and Mustard article.

Main Question


Our main question:

  1. How does the inclusion of different numbers of age groups influence the results of an analysis of right to carry laws and violence rates?

Learning Objectives


Statistical Learning Objectives:

In this case study, students will learn:
1) what multicolinearity is and how it can influence linear regression coefficients
2) how to look for the presence of multicolinarity
3) the difference between multicolinearity and correlation

Data science Learning Objectives:

  1. joining data from multiple sources (dplyr)
  2. reshaping data into different formats (tidyr)
  3. visualizations (ggplot2)

We will especially focus on using packages and functions from the Tidyverse, such as dplyr and ggplot2. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially efficient.

Context


So what exactly is a right-to-carry law?

It is a law thatspecifies if and how citizens are allowed to have a firearm on their person or nearby (for example in the citizen’s car) in public.

The Second Amendment to the United States Constitution guarantees the right to “keep and bear arms”. The amendment was ratified in 1791 as part of the Bill of Rights.

However, there are no federal laws about carrying firearms in public.

These laws are created and enforced at the state level. Sates vary greatly in their laws about the right to carry firearms. Some require extensive effort to obtain a permit to legally carry a firearm, while other states require very minimal effort to legally carry a firearm.

According to Wikipedia about the history of right-to-carry policies in the United States:

Public perception on concealed carry vs open carry has largely flipped. In the early days of the United States, open carrying of firearms, long guns and revolvers was a common and well-accepted practice. Seeing guns carried openly was not considered to be any cause for alarm. Therefore, anyone who would carry a firearm but attempt to conceal it was considered to have something to hide, and presumed to be a criminal. For this reason, concealed carry was denounced as a detestable practice in the early days of the United States.

Concealed weapons bans were passed in Kentucky and Louisiana in 1813. (In those days open carry of weapons for self-defense was considered acceptable; concealed carry was denounced as the practice of criminals.) By 1859, Indiana, Tennessee, Virginia, Alabama, and Ohio had followed suit. By the end of the nineteenth century, similar laws were passed in places such as Texas, Florida, and Oklahoma, which protected some gun rights in their state constitutions. Before the mid 1900s, most U.S. states had passed concealed carry laws rather than banning weapons completely. Until the late 1990s, many Southern states were either “No-Issue” or “Restrictive May-Issue”. Since then, these states have largely enacted “Shall-Issue” licensing laws, with numerous states legalizing “Unrestricted concealed carry”.

See here for more information.

Here are the general categories of Right to Carry Laws:

source

source

You can see that none of the fifty states have no-issue laws currently, meaning that all states allow the right to carry firearms at least in some way, however the level of restrictions is dramatically different from one state to another.

Here you can see how these laws have changed over time around the country:

There is variation from state to state even within the same general category:

For example here are the current carry laws in Idaho which is considered an “Unrestricted - no permit required” state:

Idaho permits the open carrying of firearms.

Idaho law permits both residents and non-residents who are at least 18 years old to carry concealed weapons, without a carry license, outside the limits of or confines of any city, provided the person is not otherwise disqualified from being issued a license to carry.

A person may also carry concealed weapons on or about his or her person, without a license, in the person’s own place of abode or fixed place of business, on property in which the person has any ownership or leasehold interest, or on private property where the person has permission to carry from any person who has an ownership or leasehold interest in that property.

State law also allows any resident of Idaho or a current member of the armed forces of the United States to carry a concealed handgun without a license to carry, provided the person is over 18 years old and not disqualified from being issued a license to carry concealed weapons under state law. An amendment to state law that takes effect on July 1, 2020 changes the reference in the above law from “a resident of Idaho” to “any citizen of the United States.”

And here are the current carry laws in Arizona which is also considered an “Unrestricted- - no permit required” state:

Arizona respects the right of law abiding citizens to openly carry a handgun.

Any person 21 years of age or older, who is not prohibited possessor, may carry a weapon openly or concealed without the need for a license. Any person carrying without a license must acknowledge and comply with the demands of a law enforcement officer when asked if he/she is carrying a concealed deadly weapon, if the officer has initiated an “investigation” such as a traffic stop.

Notice that citizens in Idaho only need to be 18 to carry a firearm, whereas they must be 21 in Arizona.

In contrast here is an example of current carry laws in Maryland which is considered a “Rights Restricted-Very Limited Issue” state:

Carrying and Transportation in Vehicles It is unlawful for any person without a permit to wear or carry a handgun, openly or concealed, upon or about his person. It is also unlawful for any person to knowingly transport a handgun in any vehicle traveling on public roads, highways, waterways or airways, or upon roads or parking lots generally used by the public. This does not apply to any person wearing, carrying or transporting a handgun within the confines of real estate owned or leased by him, or on which he resides, or within the confines of a business establishment owned or leased by him.

Permit To Carry Application for a permit to carry a handgun is made to the Secretary of State Police. In addition to the printed application form, the applicant should submit a notarized letter stating the reasons why he is applying for a permit.

avocado….Right to carry and covid masks?

Limitations


There are some important considerations regarding this data analysis to keep in mind:

  1. We do not use all of the data used by either the Lott and Mustard or Donohue, et al. analyses, nor do we perform the same analysis of each article. We instead perform a much simpler analysis with less variables for the purposes of illustration of the concept of multicollinearity and its influence on regression coefficients, not to reproduce either analysis.

  2. Because our analysis is an oversimplification, our analysis should not be used for determining policy changes, instead we suggest that users consult with a specialist.

We would also like to note that…AVOCADO It is important that we do not treat race as an objective measure. Despite this, it can be used to advance scientific inquiry. For more information on this topic, we have included a link to a paper on the use of race as a measure in epidemiology.

We will begin by loading the packages that we will need:

Package Use
here to easily load and save data
readr to import the CSV file data
[car] to calculate vif values

The first time we use a function, we will use the :: to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.

What are the data?


Below is a table from the Donohue, et al. paper that shows the data used in both analyses, where DAW stands for Donohue, et al. and LM stands for Lott and Mustard.

We will be using a subset of these variables, which are highlighted in green:

Data Import


Demographic and Population data

To obtain information about age, sex, and race, and overall population we will use US Census Bureau data, just like both of the articles. The cesnus data is available for different time spans. Here are the links for the years used in our analysis. We will use data from 1977 to 2010.

Data Link
years 1977 to 1979 link
years 1980 to 1989 link * county data was used for this decade
years 1990 to 1999 link
years 2000 to 2010 link
technical documentation

To import the data we will use the read_csv() function of the readr package for the csv files. In some decades, there are separate files for each year, we will read each of these together using the base list.files() function to get all of the names for each file and then the map() function of the purrr package to apply the read_csv() function on all of the file paths in the list created by list.files(). For years that are txt files we will use read_table2() also fo the readr package. The read_table2() function, unlike the read_table(), allows for any number of whitespace characters between columns, and the lines can be of different lengths.

AVOCADO I am a bit confused about the last decade… it’s only one file but it seems to need map…

[[1]]
# A tibble: 62,244 x 21
   REGION DIVISION STATE NAME    SEX ORIGIN  RACE AGEGRP ESTIMATESBASE20…
    <dbl>    <dbl> <dbl> <chr> <dbl>  <dbl> <dbl>  <dbl>            <dbl>
 1      0        0     0 Unit…     0      0     0      0        281424600
 2      0        0     0 Unit…     0      0     0      1         19176154
 3      0        0     0 Unit…     0      0     0      2         20549855
 4      0        0     0 Unit…     0      0     0      3         20528425
 5      0        0     0 Unit…     0      0     0      4         20218782
 6      0        0     0 Unit…     0      0     0      5         18962964
 7      0        0     0 Unit…     0      0     0      6         19381792
 8      0        0     0 Unit…     0      0     0      7         20511067
 9      0        0     0 Unit…     0      0     0      8         22707390
10      0        0     0 Unit…     0      0     0      9         22442442
# … with 62,234 more rows, and 12 more variables: POPESTIMATE2000 <dbl>,
#   POPESTIMATE2001 <dbl>, POPESTIMATE2002 <dbl>, POPESTIMATE2003 <dbl>,
#   POPESTIMATE2004 <dbl>, POPESTIMATE2005 <dbl>, POPESTIMATE2006 <dbl>,
#   POPESTIMATE2007 <dbl>, POPESTIMATE2008 <dbl>, POPESTIMATE2009 <dbl>,
#   CENSUS2010POP <dbl>, POPESTIMATE2010 <dbl>

Notice that the STATE variable for the demographic data is numeric. That is because it is encoded by Federal Information Processing Standard (FIPS) state codes{target="_blank". Thus we also need to import data about FIPS encoding so that we can identify what data corresponds to what state.

##State FIPS codes

The following data was downloaded from the US Census Bureau.

To import the data we will use the read_xls() function of the readxl package. Since the first five lines of this excel is information about the source of the data and when it was released, we need to skip importing these lines using the skip argument so that the data has the same number of columns for each row.

# A tibble: 64 x 4
   Region Division `State\n(FIPS)` Name                    
   <chr>  <chr>    <chr>           <chr>                   
 1 1      0        00              Northeast Region        
 2 1      1        00              New England Division    
 3 1      1        09              Connecticut             
 4 1      1        23              Maine                   
 5 1      1        25              Massachusetts           
 6 1      1        33              New Hampshire           
 7 1      1        44              Rhode Island            
 8 1      1        50              Vermont                 
 9 1      2        00              Middle Atlantic Division
10 1      2        34              New Jersey              
# … with 54 more rows

Police staffing data

The following data was downloaded from the Federal Bureau of Investigation.

The read_csv() function of the readr package guesses what the class is for each variable, but sometimes it makes mistakes. It is good to specify the class for variables if you know them. We know that we want the variables about male and female counts to be numeric. We can specify that using the col_types = argument. See here and here for more information.

# A tibble: 6 x 21
  data_year ori   pub_agency_name pub_agency_unit state_abbr division_name
      <dbl> <chr> <chr>           <chr>           <chr>      <chr>        
1      1960 AK02… Alcohol Bevera… <NA>            AK         Pacific      
2      1960 AL00… Homewood        <NA>            AL         East South C…
3      1960 AL01… Coffeeville     <NA>            AL         East South C…
4      1960 AL01… Coffee          <NA>            AL         East South C…
5      1960 AL02… Mentone         <NA>            AL         East South C…
6      1960 AL03… Greensboro      <NA>            AL         East South C…
# … with 15 more variables: region_name <chr>, county_name <chr>,
#   agency_type_name <chr>, population_group_desc <chr>, population <dbl>,
#   male_officer_ct <dbl>, male_civilian_ct <dbl>, male_total_ct <dbl>,
#   female_officer_ct <lgl>, female_civilian_ct <lgl>, female_total_ct <dbl>,
#   officer_ct <lgl>, civilian_ct <lgl>, total_pe_ct <lgl>,
#   pe_ct_per_1000 <lgl>

Unemplyment data

The following data was downloaded from the U.S. Bureau of Labor Statistics.

There are excel files for each state. As you can see, there are many rows to skip to make sure that there are the same number of columns for each row. We can also see that the state name is located in a couple of the first rows.

We can also see that here if we just try to read in the files directly.

[[1]]
# A tibble: 55 x 14
   `Local Area Une… ...2  ...3  ...4  ...5  ...6  ...7  ...8  ...9  ...10 ...11
   <chr>            <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
 1 Original Data V… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 2 <NA>             <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 3 Series Id:       LAUS… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 4 Not Seasonally … <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 5 Area:            Alab… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 6 Area Type:       Stat… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 7 State/Region/Di… Alab… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 8 Measure:         unem… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
 9 Years:           1977… <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
10 <NA>             <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
# … with 45 more rows, and 3 more variables: ...12 <chr>, ...13 <chr>,
#   ...14 <chr>

So now we will skip the first 10 lines. And also create a names tibble that contains only the cell with the state information.

[[1]]
# A tibble: 44 x 14
    Year   Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1  1977   7.5   9     7.7   7.2   6.8   8.6   8     7.8   6.7   6.3   6.3   6  
 2  1978   7.1   6.9   6.2   5.4   5.1   6.9   6.7   6.7   6.5   6.3   6.3   6.5
 3  1979   6.7   7.5   6.9   6.6   6.4   8.4   7.7   7.8   7.1   7.2   6.9   6.7
 4  1980   7.7   7.8   7.4   7.4   8.4   9.7  10.4  10.3   9.3   9.6   9.4   9  
 5  1981  10    10.3   9.5   9.1   9.4  11.1  10.4  10.9  10.8  11.7  11.5  11.8
 6  1982  13.2  13.2  12.9  12.6  12.8  14.5  14.7  14.8  14.7  15.1  15.4  15.3
 7  1983  16    16    14.5  13.7  13.3  14.6  13.9  13.8  13.2  12.8  12.1  11.8
 8  1984  12.5  12.4  11.4  10.8  10.1  11.3  11.5  11.3  10.8  10.2   9.7  10.1
 9  1985  10.7  10.5   9.8   8.7   8.4   9.6   9.2   8.8   8.6   8.6   8.4   8.7
10  1986   9.3  10.4  10.1   9.4   9.4  10.5   9.7   9.6   9.7   9.7   9.6   9  
# … with 34 more rows, and 1 more variable: Annual <dbl>

To get the state name for each file using the map() function to perform functions across all of the files, we will specifically import only a small range of cells using the range = argument and then grab the cell that has state information based on it’s location within the range of cells imported using c() and then use the base unlist() function to unlist the list that this creates.

 [1] "Alabama"              "Alaska"               "Arizona"             
 [4] "Arkansas"             "California"           "Colorado"            
 [7] "Connecticut"          "Delaware"             "District of Columbia"
[10] "Florida"              "Georgia"              "Hawaii"              
[13] "Idaho"                "Illinois"             "Indiana"             
[16] "Iowa"                 "Kansas"               "Kentucky"            
[19] "Louisiana"            "Maine"                "Maryland"            
[22] "Massachusetts"        "Michigan"             "Minnesota"           
[25] "Mississippi"          "Missouri"             "Montana"             
[28] "Nebraska"             "Nevada"               "New Hampshire"       
[31] "New Jersey"           "New Mexico"           "New York"            
[34] "North Carolina"       "North Dakota"         "Ohio"                
[37] "Oklahoma"             "Oregon"               "Pennsylvania"        
[40] "Rhode Island"         "South Carolina"       "South Dakota"        
[43] "Tennessee"            "Texas"                "Utah"                
[46] "Vermont"              "Virginia"             "Washington"          
[49] "West Virginia"        "Wisconsin"            "Wyoming"             

Poverty data

Extracted from Table 21 from US Census Bureau Poverty Data

AVOCado strange issue

# A tibble: 6 x 6
  `NOTE: Number in thousa… ...2  ...3   ...4         ...5         ...6          
  <chr>                    <chr> <chr>  <chr>        <chr>        <chr>         
1 2018                     <NA>  <NA>    <NA>        <NA>          <NA>         
2 STATE                    Total Number "Standard\n… Percent      "Standard\ner…
3 Alabama                  4877  779    "65"         16           "1.3"         
4 Alaska                   720   94     "9"          13.1         "1.2"         
5 Arizona                  7241  929    "80"         12.80000000… "1.1000000000…
6 Arkansas                 2912  462    "38"         15.9         "1.3"         

We can see that this will require some wranlging to make the data more usable.

Violent crime

Violent crime data was obtained from here This data is a bit trickier because of spaces and / in the column names, thus the read_lines() function of the readr package works better than the read_csv() function.

[1] "Estimated crime in Alabama"                                                                                                           
[2] "\n,,National or state crime,,,,,,,"                                                                                                   
[3] "\n,,Violent crime,,,,,,,"                                                                                                             
[4] "\nYear,Population,Violent crime total,Murder and nonnegligent Manslaughter,Legacy rape /1,Revised rape /2,Robbery,Aggravated assault,"
[5] "1977,   3690000,      15293,         524,         929,,       3572,      10268 "                                                      
[6] "1978,   3742000,      15682,         499,         954,,       3708,      10521 "                                                      

We can see that this data will also require some wranlging to make it more usable.

Right-to-carry data

This data is extracted from table in Donohue paper. We will use the function pdf_text() of the pdftools package to import the pdf document.

[1] "                                NBER WORKING PAPER SERIES\n                      RIGHT-TO-CARRY LAWS AND VIOLENT CRIME:\n              A COMPREHENSIVE ASSESSMENT USING PANEL DATA AND\n                    A STATE-LEVEL SYNTHETIC CONTROL ANALYSIS\n                                          John J. Donohue\n                                            Abhay Aneja\n                                           Kyle D. Weber\n                                         Working Paper 23510\n                                 http://www.nber.org/papers/w23510\n                      NATIONAL BUREAU OF ECONOMIC RESEARCH\n                                     1050 Massachusetts Avenue\n                                        Cambridge, MA 02138\n                                 June 2017, Revised November 2018\nPreviously circulated as \"Right-to-Carry Laws and Violent Crime: A Comprehensive Assessment\nUsing Panel Data and a State-Level Synthetic Controls Analysis.\" We thank Dan Ho, Stefano\nDellaVigna, Rob Tibshirani, Trevor Hastie, StefanWager, Jeff Strnad, and participants at the\n2011 Conference of Empirical Legal Studies (CELS), 2012 American Law and Economics\nAssociation (ALEA) Annual Meeting, 2013 Canadian Law and Economics Association (CLEA)\nAnnual Meeting, 2015 NBER Summer Institute (Crime), and the Stanford Law School faculty\nworkshop for their comments and helpful suggestions. Financial support was provided by\nStanford Law School. We are indebted to Alberto Abadie, Alexis Diamond, and Jens\nHainmueller for their work developing the synthetic control algorithm and programming the Stata\nmodule used in this paper and for their helpful comments. The authors would also like to thank\nAlex Albright, Andrew Baker, Jacob Dorn, Bhargav Gopal, Crystal Huang, Mira Korb, Haksoo\nLee, Isaac Rabbani, Akshay Rao, Vikram Rao, Henrik Sachs and Sidharth Sah who provided\nexcellent research assistance, as well as Addis O’Connor and Alex Chekholko at the Research\nComputing division of Stanford’s Information Technology Services for their technical support.\nThe views expressed herein are those of the author and do not necessarily reflect the views of the\nNational Bureau of Economic Research.\nNBER working papers are circulated for discussion and comment purposes. They have not been\npeer-reviewed or been subject to the review by the NBER Board of Directors that accompanies\nofficial NBER publications.\n© 2017 by John J. Donohue, Abhay Aneja, and Kyle D. Weber. All rights reserved. Short\nsections of text, not to exceed two paragraphs, may be quoted without explicit permission\nprovided that full credit, including © notice, is given to the source.\n"

Again, this data will also require quite a bit of wrangling.

Data Wrangling


State FIPS codes

Let’s take a look at our state FIPS data to see if it needs any cleaning or reshaping.

# A tibble: 6 x 4
  Region Division `State\n(FIPS)` Name                
  <chr>  <chr>    <chr>           <chr>               
1 1      0        00              Northeast Region    
2 1      1        00              New England Division
3 1      1        09              Connecticut         
4 1      1        23              Maine               
5 1      1        25              Massachusetts       
6 1      1        33              New Hampshire       

We only need the last two columns, but we might want to rename them. The Name variable is vague. The variable with the FIPS code is called State\n(FIPS). To get rid of the new line in this variable name and to change the Name variable to something more informative, we will use the rename() function of the dplyr package. To use this function, we need to list the new name first followed by = and then the existing variable. We can rename multiple variables at the same time by using a comma to separate the variables we are renaming. We will use the select() function also of the dplyr package just to keep these variables, and we will filter out the rows with FIPS values of 00 with the filter() function, agian also part of the dplyr package. we will specify that we want STATEFP values that are not equal to 00 by using this operator: !=. We will also use the double pipe operator %<>% of the magrittr package which allows us to use data as iuput and then reassign it after we peform sum functions using it.

# A tibble: 51 x 2
   STATEFP STATE        
   <chr>   <chr>        
 1 09      Connecticut  
 2 23      Maine        
 3 25      Massachusetts
 4 33      New Hampshire
 5 44      Rhode Island 
 6 50      Vermont      
 7 34      New Jersey   
 8 36      New York     
 9 42      Pennsylvania 
10 17      Illinois     
# … with 41 more rows

Demographics

1977-1979

Now let’s take a look at our demographic data across the decades that we wish to study. If you have very wide data (meaning it has many columns), one way to view the data so that you can see all of the columns at the same time is to use the glimpse() function of the dplyr package.

Taking a look at the first decade of data, we can see that the Race/Sex Indicator contains two types of data, the race and the sex. This does not follow the tidy data philosophy, where each cell of a tibble should only contain one piece of information. Typically one might think of using the separate() function of the tidyr package to split this variable into two. However, one of the race values is Other races and since this also has a space, this makes separating this data more tricky.

Instead we will use the str_extract() function of the stringr package and the mutate() function of the dplyr package. The “mutate()” will allow us to create new variables, and “str_extract()” function will allow us to match specific patterns and pull out matches to those patterns. Therefore, if the Race/Sex Indicator value is Other races male and if we extract patterns matching either "male" or "female" which we can specify like this pattern = "male|female" then, the value will be male.

First we need to rename the Race/Sex Indicator varaible to not have spaces so that it is compatible with the str_extract() function.

We also want to rename a couple of variables to be simpler and filter the data to only include the years of the data we are interested in, as well as remove some variables that we dont need like the FIPS State Code. We can remove variables by using the select() function with a - minus sign in front of the variable we wish to remove.

Rows: 3,060
Columns: 22
$ `Year of Estimate`   <dbl> 1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970, …
$ `FIPS State Code`    <chr> "01", "01", "01", "01", "01", "01", "02", "02", …
$ `State Name`         <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Ala…
$ `Race/Sex Indicator` <chr> "White male", "White female", "Black male", "Bla…
$ `Under 5 years`      <dbl> 105856, 100613, 47403, 47079, 244, 250, 12382, 1…
$ `5 to 9 years`       <dbl> 120876, 115194, 55443, 54851, 255, 251, 13888, 1…
$ `10 to 14 years`     <dbl> 129091, 122352, 60427, 60065, 253, 245, 13255, 1…
$ `15 to 19 years`     <dbl> 119500, 116107, 52921, 55144, 281, 254, 11179, 9…
$ `20 to 24 years`     <dbl> 103665, 108513, 29948, 35165, 413, 331, 20237, 1…
$ `25 to 29 years`     <dbl> 86538, 88359, 19535, 23662, 239, 302, 12538, 107…
$ `30 to 34 years`     <dbl> 74452, 77595, 17196, 22021, 236, 284, 10331, 865…
$ `35 to 39 years`     <dbl> 71511, 74941, 16654, 22248, 161, 279, 9548, 7510…
$ `40 to 44 years`     <dbl> 75242, 78908, 17564, 24249, 127, 253, 8282, 6353…
$ `45 to 49 years`     <dbl> 73874, 78589, 18186, 23028, 108, 148, 6995, 5820…
$ `50 to 54 years`     <dbl> 68048, 72481, 17618, 22104, 95, 100, 5609, 4494,…
$ `55 to 59 years`     <dbl> 61071, 67699, 18118, 21909, 88, 93, 4029, 2986, …
$ `60 to 64 years`     <dbl> 52361, 61065, 16456, 20068, 69, 94, 2392, 1830, …
$ `65 to 69 years`     <dbl> 38977, 49685, 14498, 19364, 54, 73, 1292, 965, 2…
$ `70 to 74 years`     <dbl> 26767, 37227, 9541, 12509, 70, 66, 602, 496, 8, …
$ `75 to 79 years`     <dbl> 17504, 27163, 6030, 8291, 31, 52, 326, 305, 1, 5…
$ `80 to 84 years`     <dbl> 9937, 16470, 3485, 5031, 37, 30, 211, 186, 4, 5,…
$ `85 years and over`  <dbl> 5616, 10445, 2448, 4035, 76, 29, 143, 126, 19, 4…
Rows: 918
Columns: 22
$ YEAR                <dbl> 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1…
$ STATE               <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alab…
$ `Under 5 years`     <dbl> 98814, 94595, 46201, 45784, 590, 621, 14316, 1353…
$ `5 to 9 years`      <dbl> 113365, 107395, 50097, 49329, 672, 660, 14621, 13…
$ `10 to 14 years`    <dbl> 123107, 116182, 54925, 53955, 677, 653, 14795, 13…
$ `15 to 19 years`    <dbl> 135343, 130433, 58468, 59926, 674, 605, 15207, 13…
$ `20 to 24 years`    <dbl> 126053, 125352, 43898, 51433, 722, 773, 20106, 16…
$ `25 to 29 years`    <dbl> 111547, 112471, 31014, 36648, 638, 835, 20444, 18…
$ `30 to 34 years`    <dbl> 100674, 101543, 22528, 26694, 571, 766, 17514, 15…
$ `35 to 39 years`    <dbl> 81038, 83369, 17473, 22213, 498, 586, 13098, 1069…
$ `40 to 44 years`    <dbl> 75042, 77793, 16446, 22146, 356, 479, 10067, 7935…
$ `45 to 49 years`    <dbl> 76296, 79753, 16578, 22576, 295, 432, 8460, 6848,…
$ `50 to 54 years`    <dbl> 74844, 81079, 17117, 23028, 206, 326, 7268, 5914,…
$ `55 to 59 years`    <dbl> 67785, 75905, 16437, 21435, 166, 213, 5398, 4485,…
$ `60 to 64 years`    <dbl> 58853, 69406, 16276, 21075, 145, 174, 3349, 2708,…
$ `65 to 69 years`    <dbl> 48848, 62430, 15837, 21126, 107, 173, 1714, 1468,…
$ `70 to 74 years`    <dbl> 34475, 50075, 11450, 16028, 90, 138, 915, 928, 22…
$ `75 to 79 years`    <dbl> 20977, 34027, 7601, 10825, 53, 106, 500, 493, 10,…
$ `80 to 84 years`    <dbl> 10831, 21483, 3896, 6272, 25, 49, 237, 268, 4, 7,…
$ `85 years and over` <dbl> 6683, 15729, 2667, 5426, 33, 41, 153, 211, 11, 6,…
$ SEX                 <chr> "male", "female", "male", "female", "male", "fema…
$ RACE                <chr> "White", "White", "Black", "Black", "Other", "Oth…

That’s looking pretty good! We also want to take all the age group variabels and make one variable that is the age group name and one that is the value of the population count for that age group. To do this we will use the pivot_longer() function of the tidyr package. To use this function, we need to use the cols argument to indicate which columns we want to pivot. We also name the new variables we will create with the names_to and values_to arguments. The names_to will be the name of the variable that will identify each age group and values_to will be the name of the variable that contains the corresponding population values.

Rows: 16,524
Columns: 6
$ YEAR      <dbl> 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977, 1977,…
$ STATE     <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Ala…
$ SEX       <chr> "male", "male", "male", "male", "male", "male", "male", "ma…
$ RACE      <chr> "White", "White", "White", "White", "White", "White", "Whit…
$ AGE_GROUP <chr> "Under 5 years", "5 to 9 years", "10 to 14 years", "15 to 1…
$ SUB_POP   <dbl> 98814, 113365, 123107, 135343, 126053, 111547, 100674, 8103…

We also want to get data about the total population for the state for each year.

To do so we can sum all the values for the SUB_POP variable that we just created. To do this we can use the group_by and summarize() functions of the dplyr package. The group_by() function specifies how we want to calculate our sum, that we would like to calculate it for each year and each state individually. Thus, all the values that have the same STATE and YEAR values will be summed together, rather than summing using all of the values in the SUB_POP variable. The .groups argument allows us to remove the grouping after we peform the calculation with summarize().

# A tibble: 153 x 3
    YEAR STATE                 TOT_POP
   <dbl> <chr>                   <dbl>
 1  1977 Alabama               3782571
 2  1977 Alaska                 397220
 3  1977 Arizona               2427296
 4  1977 Arkansas              2207195
 5  1977 California           22350332
 6  1977 Colorado              2696179
 7  1977 Connecticut           3088745
 8  1977 Delaware               594815
 9  1977 District of Columbia   681766
10  1977 Florida               8888806
# … with 143 more rows

Now we will add the population value to the demographic tibble using the left_join() function of the dplyr package. It is imporant that we specify how this should be done, that the YEAR and STATE variable vlaues should match eachother. This will place the dem_77_79 variables to the left of the pop_77_79 data.

# A tibble: 16,524 x 7
    YEAR STATE   SEX   RACE  AGE_GROUP      SUB_POP TOT_POP
   <dbl> <chr>   <chr> <chr> <chr>            <dbl>   <dbl>
 1  1977 Alabama male  White Under 5 years    98814 3782571
 2  1977 Alabama male  White 5 to 9 years    113365 3782571
 3  1977 Alabama male  White 10 to 14 years  123107 3782571
 4  1977 Alabama male  White 15 to 19 years  135343 3782571
 5  1977 Alabama male  White 20 to 24 years  126053 3782571
 6  1977 Alabama male  White 25 to 29 years  111547 3782571
 7  1977 Alabama male  White 30 to 34 years  100674 3782571
 8  1977 Alabama male  White 35 to 39 years   81038 3782571
 9  1977 Alabama male  White 40 to 44 years   75042 3782571
10  1977 Alabama male  White 45 to 49 years   76296 3782571
# … with 16,514 more rows

We will also calculate the percentage that each group makes up of the total population, by dividing the SUB_POP by the TOT_POP and multiplying by 100 using the mutate() function. we will also remove the other population variables.

# A tibble: 16,524 x 6
    YEAR STATE   SEX   RACE  AGE_GROUP      PERC_SUB_POP
   <dbl> <chr>   <chr> <chr> <chr>                 <dbl>
 1  1977 Alabama male  White Under 5 years          2.61
 2  1977 Alabama male  White 5 to 9 years           3.00
 3  1977 Alabama male  White 10 to 14 years         3.25
 4  1977 Alabama male  White 15 to 19 years         3.58
 5  1977 Alabama male  White 20 to 24 years         3.33
 6  1977 Alabama male  White 25 to 29 years         2.95
 7  1977 Alabama male  White 30 to 34 years         2.66
 8  1977 Alabama male  White 35 to 39 years         2.14
 9  1977 Alabama male  White 40 to 44 years         1.98
10  1977 Alabama male  White 45 to 49 years         2.02
# … with 16,514 more rows

1980-1989

For this decade each year is a separate tibble and they are combined as a list.

[1] "list"

So the first thing we need to do is combine each tibble of the list together. We can do that using the bind_rows() function of dplyr which appends the data together based on the presence of columns with the same name in the different tibbles. We will use the map_df() function of the purrr package to allow us to do this across each tibble in our list.

Rows: 188,460
Columns: 21
$ `Year of Estimate`            <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 198…
$ `FIPS State and County Codes` <chr> "01001", "01001", "01001", "01001", "01…
$ `Race/Sex Indicator`          <chr> "White male", "White female", "Black ma…
$ `Under 5 years`               <dbl> 985, 831, 357, 346, 4, 7, 2422, 2346, 6…
$ `5 to 9 years`                <dbl> 1096, 987, 427, 395, 9, 8, 2661, 2467, …
$ `10 to 14 years`              <dbl> 1271, 1074, 395, 415, 4, 11, 2783, 2614…
$ `15 to 19 years`              <dbl> 1308, 1259, 460, 429, 10, 5, 3049, 2841…
$ `20 to 24 years`              <dbl> 972, 1006, 300, 380, 3, 3, 2423, 2428, …
$ `25 to 29 years`              <dbl> 850, 912, 240, 235, 2, 11, 2372, 2475, …
$ `30 to 34 years`              <dbl> 891, 983, 163, 196, 4, 10, 2410, 2400, …
$ `35 to 39 years`              <dbl> 942, 1015, 120, 158, 3, 12, 2101, 2202,…
$ `40 to 44 years`              <dbl> 854, 882, 133, 147, 2, 11, 1881, 1859, …
$ `45 to 49 years`              <dbl> 828, 739, 107, 154, 4, 11, 1708, 1694, …
$ `50 to 54 years`              <dbl> 631, 602, 113, 165, 1, 7, 1657, 1798, 2…
$ `55 to 59 years`              <dbl> 524, 532, 113, 150, 1, 2, 1641, 1943, 1…
$ `60 to 64 years`              <dbl> 428, 451, 126, 166, 0, 1, 1630, 1819, 1…
$ `65 to 69 years`              <dbl> 358, 417, 128, 160, 1, 0, 1503, 1729, 1…
$ `70 to 74 years`              <dbl> 242, 332, 87, 119, 0, 0, 1163, 1335, 16…
$ `75 to 79 years`              <dbl> 123, 237, 70, 94, 0, 0, 671, 906, 87, 1…
$ `80 to 84 years`              <dbl> 52, 137, 31, 57, 0, 0, 331, 527, 43, 67…
$ `85 years and over`           <dbl> 39, 86, 13, 44, 0, 1, 187, 408, 27, 65,…

Great! Now our data is all together.

Now we will wrangle the data similarly to the previous decade.

Rows: 188,460
Columns: 22
$ YEAR                          <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 198…
$ `FIPS State and County Codes` <chr> "01001", "01001", "01001", "01001", "01…
$ `Under 5 years`               <dbl> 985, 831, 357, 346, 4, 7, 2422, 2346, 6…
$ `5 to 9 years`                <dbl> 1096, 987, 427, 395, 9, 8, 2661, 2467, …
$ `10 to 14 years`              <dbl> 1271, 1074, 395, 415, 4, 11, 2783, 2614…
$ `15 to 19 years`              <dbl> 1308, 1259, 460, 429, 10, 5, 3049, 2841…
$ `20 to 24 years`              <dbl> 972, 1006, 300, 380, 3, 3, 2423, 2428, …
$ `25 to 29 years`              <dbl> 850, 912, 240, 235, 2, 11, 2372, 2475, …
$ `30 to 34 years`              <dbl> 891, 983, 163, 196, 4, 10, 2410, 2400, …
$ `35 to 39 years`              <dbl> 942, 1015, 120, 158, 3, 12, 2101, 2202,…
$ `40 to 44 years`              <dbl> 854, 882, 133, 147, 2, 11, 1881, 1859, …
$ `45 to 49 years`              <dbl> 828, 739, 107, 154, 4, 11, 1708, 1694, …
$ `50 to 54 years`              <dbl> 631, 602, 113, 165, 1, 7, 1657, 1798, 2…
$ `55 to 59 years`              <dbl> 524, 532, 113, 150, 1, 2, 1641, 1943, 1…
$ `60 to 64 years`              <dbl> 428, 451, 126, 166, 0, 1, 1630, 1819, 1…
$ `65 to 69 years`              <dbl> 358, 417, 128, 160, 1, 0, 1503, 1729, 1…
$ `70 to 74 years`              <dbl> 242, 332, 87, 119, 0, 0, 1163, 1335, 16…
$ `75 to 79 years`              <dbl> 123, 237, 70, 94, 0, 0, 671, 906, 87, 1…
$ `80 to 84 years`              <dbl> 52, 137, 31, 57, 0, 0, 331, 527, 43, 67…
$ `85 years and over`           <dbl> 39, 86, 13, 44, 0, 1, 187, 408, 27, 65,…
$ SEX                           <chr> "male", "female", "male", "female", "ma…
$ RACE                          <chr> "White", "White", "Black", "Black", "Ot…

Notice that this time the state information is based on the numeric FIPS value. We want only the first two values, as the rest indicate the county. We can use the str_sub() function of the stringr package for this. We will specify that we want to start at the first position and end at the second. Just like str_extract() we need to rename this variable first so that it is compatible.

Rows: 188,460
Columns: 23
$ YEAR                <dbl> 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1980, 1…
$ STATEFP_temp        <chr> "01001", "01001", "01001", "01001", "01001", "010…
$ `Under 5 years`     <dbl> 985, 831, 357, 346, 4, 7, 2422, 2346, 672, 645, 3…
$ `5 to 9 years`      <dbl> 1096, 987, 427, 395, 9, 8, 2661, 2467, 740, 680, …
$ `10 to 14 years`    <dbl> 1271, 1074, 395, 415, 4, 11, 2783, 2614, 644, 670…
$ `15 to 19 years`    <dbl> 1308, 1259, 460, 429, 10, 5, 3049, 2841, 711, 762…
$ `20 to 24 years`    <dbl> 972, 1006, 300, 380, 3, 3, 2423, 2428, 516, 601, …
$ `25 to 29 years`    <dbl> 850, 912, 240, 235, 2, 11, 2372, 2475, 414, 469, …
$ `30 to 34 years`    <dbl> 891, 983, 163, 196, 4, 10, 2410, 2400, 303, 352, …
$ `35 to 39 years`    <dbl> 942, 1015, 120, 158, 3, 12, 2101, 2202, 224, 260,…
$ `40 to 44 years`    <dbl> 854, 882, 133, 147, 2, 11, 1881, 1859, 206, 288, …
$ `45 to 49 years`    <dbl> 828, 739, 107, 154, 4, 11, 1708, 1694, 219, 236, …
$ `50 to 54 years`    <dbl> 631, 602, 113, 165, 1, 7, 1657, 1798, 203, 261, 7…
$ `55 to 59 years`    <dbl> 524, 532, 113, 150, 1, 2, 1641, 1943, 178, 219, 8…
$ `60 to 64 years`    <dbl> 428, 451, 126, 166, 0, 1, 1630, 1819, 171, 209, 8…
$ `65 to 69 years`    <dbl> 358, 417, 128, 160, 1, 0, 1503, 1729, 170, 232, 6…
$ `70 to 74 years`    <dbl> 242, 332, 87, 119, 0, 0, 1163, 1335, 164, 182, 4,…
$ `75 to 79 years`    <dbl> 123, 237, 70, 94, 0, 0, 671, 906, 87, 129, 3, 6, …
$ `80 to 84 years`    <dbl> 52, 137, 31, 57, 0, 0, 331, 527, 43, 67, 1, 2, 56…
$ `85 years and over` <dbl> 39, 86, 13, 44, 0, 1, 187, 408, 27, 65, 1, 1, 30,…
$ SEX                 <chr> "male", "female", "male", "female", "male", "fema…
$ RACE                <chr> "White", "White", "Black", "Black", "Other", "Oth…
$ STATE               <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alab…

AVOCADO - why do we group by age_group sex and race this time but not with the last decade?

# A tibble: 55,080 x 6
    YEAR STATE   AGE_GROUP      SEX    RACE  SUB_POP
   <dbl> <chr>   <chr>          <chr>  <chr>   <dbl>
 1  1980 Alabama 10 to 14 years female Black   50108
 2  1980 Alabama 10 to 14 years female Other     805
 3  1980 Alabama 10 to 14 years female White  109066
 4  1980 Alabama 10 to 14 years male   Black   50768
 5  1980 Alabama 10 to 14 years male   Other     826
 6  1980 Alabama 10 to 14 years male   White  115988
 7  1980 Alabama 15 to 19 years female Black   58428
 8  1980 Alabama 15 to 19 years female Other     743
 9  1980 Alabama 15 to 19 years female White  126783
10  1980 Alabama 15 to 19 years male   Black   56808
# … with 55,070 more rows
# A tibble: 55,080 x 6
    YEAR STATE   AGE_GROUP      SEX    RACE  PERC_SUB_POP
   <dbl> <chr>   <chr>          <chr>  <chr>        <dbl>
 1  1980 Alabama 10 to 14 years female Black       1.28  
 2  1980 Alabama 10 to 14 years female Other       0.0206
 3  1980 Alabama 10 to 14 years female White       2.80  
 4  1980 Alabama 10 to 14 years male   Black       1.30  
 5  1980 Alabama 10 to 14 years male   Other       0.0212
 6  1980 Alabama 10 to 14 years male   White       2.97  
 7  1980 Alabama 15 to 19 years female Black       1.50  
 8  1980 Alabama 15 to 19 years female Other       0.0191
 9  1980 Alabama 15 to 19 years female White       3.25  
10  1980 Alabama 15 to 19 years male   Black       1.46  
# … with 55,070 more rows

1990-1999

Rows: 43,870
Columns: 19
$ Year     <dbl> NA, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 1990, 19…
$ e        <chr> NA, "01", "01", "01", "01", "01", "01", "01", "01", "01", "0…
$ Age      <dbl> NA, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16…
$ Male     <dbl> NA, 20406, 19393, 18990, 19246, 19502, 19560, 19091, 19605, …
$ Female   <dbl> NA, 19101, 18114, 18043, 17786, 18366, 18386, 18047, 18316, …
$ Male_1   <dbl> NA, 9794, 9475, 9097, 9002, 9076, 9169, 8919, 9219, 9247, 10…
$ Female_1 <dbl> NA, 9414, 9247, 8837, 8701, 8989, 9093, 8736, 9192, 9108, 97…
$ Male_2   <dbl> NA, 103, 87, 97, 94, 108, 128, 160, 178, 166, 205, 194, 179,…
$ Female_2 <dbl> NA, 90, 93, 100, 115, 114, 130, 134, 162, 155, 193, 185, 202…
$ Male_3   <dbl> NA, 192, 146, 175, 150, 168, 170, 183, 171, 136, 177, 169, 1…
$ Female_3 <dbl> NA, 170, 182, 160, 157, 178, 158, 173, 177, 185, 179, 171, 1…
$ Male_4   <dbl> NA, 223, 190, 198, 186, 190, 210, 188, 178, 182, 221, 194, 1…
$ Female_4 <dbl> NA, 220, 196, 173, 191, 190, 170, 172, 179, 173, 166, 175, 1…
$ Male_5   <dbl> NA, 47, 41, 32, 35, 36, 30, 28, 27, 29, 32, 31, 33, 34, 32, …
$ Female_5 <dbl> NA, 45, 47, 41, 30, 26, 37, 23, 35, 31, 28, 38, 22, 39, 29, …
$ Male_6   <dbl> NA, 1, 2, 1, 9, 5, 8, 2, 4, 6, 6, 0, 1, 9, 6, 7, 5, 2, 2, 4,…
$ Female_6 <dbl> NA, 8, 0, 2, 1, 4, 5, 3, 4, 4, 3, 4, 2, 2, 7, 0, 2, 2, 1, 6,…
$ Male_7   <dbl> NA, 5, 7, 2, 3, 5, 11, 2, 7, 12, 10, 7, 5, 6, 5, 6, 6, 2, 11…
$ Female_7 <dbl> NA, 5, 5, 5, 3, 14, 6, 7, 6, 3, 11, 5, 5, 7, 8, 6, 6, 7, 3, …
[1] 43870    19
# A tibble: 2 x 2
   n_na     n
  <dbl> <int>
1     0 43860
2    19    10
       YEAR     STATEFP         Age         W_M         W_F         B_M 
  "numeric" "character"   "numeric"   "numeric"   "numeric"   "numeric" 
        B_F      AIAN_M      AIAN_F       API_M       API_F 
  "numeric"   "numeric"   "numeric"   "numeric"   "numeric" 
   10 to 14 years    15 to 19 years    20 to 24 years    25 to 29 years 
             3060              3060              3060              3060 
   30 to 34 years    35 to 39 years    40 to 44 years    45 to 49 years 
             3060              3060              3060              3060 
     5 to 9 years    50 to 54 years    55 to 59 years    60 to 64 years 
             3060              3060              3060              3060 
   65 to 69 years    70 to 74 years    75 to 79 years    80 to 84 years 
             3060              3060              3060              3060 
85 years and over     Under 5 years 
             3060              3060 
       YEAR     STATEFP         W_M         W_F         B_M         B_F 
  "numeric" "character"   "numeric"   "numeric"   "numeric"   "numeric" 
     AIAN_M      AIAN_F       API_M       API_F   AGE_GROUP 
  "numeric"   "numeric"   "numeric"   "numeric" "character" 

2000-2010

Rows: 62,244
Columns: 21
$ REGION            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ DIVISION          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ STATE             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ NAME              <chr> "United States", "United States", "United States", …
$ SEX               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ORIGIN            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ RACE              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ AGEGRP            <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 1…
$ ESTIMATESBASE2000 <dbl> 281424600, 19176154, 20549855, 20528425, 20218782, …
$ POPESTIMATE2000   <dbl> 282162411, 19178293, 20463852, 20637696, 20294955, …
$ POPESTIMATE2001   <dbl> 284968955, 19298217, 20173362, 20978678, 20456284, …
$ POPESTIMATE2002   <dbl> 287625193, 19429192, 19872417, 21261421, 20610370, …
$ POPESTIMATE2003   <dbl> 290107933, 19592446, 19620851, 21415353, 20797166, …
$ POPESTIMATE2004   <dbl> 292805298, 19785885, 19454237, 21411680, 21102552, …
$ POPESTIMATE2005   <dbl> 295516599, 19917400, 19389067, 21212579, 21486214, …
$ POPESTIMATE2006   <dbl> 298379912, 19938883, 19544688, 21033138, 21807709, …
$ POPESTIMATE2007   <dbl> 301231207, 20125962, 19714611, 20841042, 22067816, …
$ POPESTIMATE2008   <dbl> 304093966, 20271127, 19929602, 20706655, 22210880, …
$ POPESTIMATE2009   <dbl> 306771529, 20244518, 20182499, 20660564, 22192810, …
$ CENSUS2010POP     <dbl> 308745538, 20201362, 20348657, 20677194, 22040343, …
$ POPESTIMATE2010   <dbl> 309349689, 20200529, 20382409, 20694011, 21959087, …
 [1] "STATE"           "SEX"             "RACE"            "AGE_GROUP"      
 [5] "POPESTIMATE2000" "POPESTIMATE2001" "POPESTIMATE2002" "POPESTIMATE2003"
 [9] "POPESTIMATE2004" "POPESTIMATE2005" "POPESTIMATE2006" "POPESTIMATE2007"
[13] "POPESTIMATE2008" "POPESTIMATE2009" "POPESTIMATE2010"
      STATE         SEX        RACE   AGE_GROUP        YEAR     SUB_POP 
"character" "character" "character" "character"   "numeric"   "numeric" 
# A tibble: 1 x 2
  poss_error     n
  <lgl>      <int>
1 FALSE        561

Combining demographic data

[1] TRUE
[1] TRUE
[1] TRUE
# A tibble: 6 x 6
   YEAR STATE   SEX   RACE  AGE_GROUP      PERC_SUB_POP
  <dbl> <chr>   <chr> <chr> <chr>                 <dbl>
1  1977 Alabama male  White Under 5 years          2.61
2  1977 Alabama male  White 5 to 9 years           3.00
3  1977 Alabama male  White 10 to 14 years         3.25
4  1977 Alabama male  White 15 to 19 years         3.58
5  1977 Alabama male  White 20 to 24 years         3.33
6  1977 Alabama male  White 25 to 29 years         2.95
# A tibble: 6 x 6
   YEAR STATE   AGE_GROUP      SEX    RACE  PERC_SUB_POP
  <dbl> <chr>   <chr>          <chr>  <chr>        <dbl>
1  1980 Alabama 10 to 14 years female Black       1.28  
2  1980 Alabama 10 to 14 years female Other       0.0206
3  1980 Alabama 10 to 14 years female White       2.80  
4  1980 Alabama 10 to 14 years male   Black       1.30  
5  1980 Alabama 10 to 14 years male   Other       0.0212
6  1980 Alabama 10 to 14 years male   White       2.97  
# A tibble: 6 x 6
   YEAR AGE_GROUP      RACE  SEX    STATE   PERC_SUB_POP
  <dbl> <chr>          <chr> <chr>  <chr>          <dbl>
1  1990 10 to 14 years White Male   Alabama       2.46  
2  1990 10 to 14 years White Female Alabama       2.33  
3  1990 10 to 14 years Black Male   Alabama       1.21  
4  1990 10 to 14 years Black Female Alabama       1.20  
5  1990 10 to 14 years Other Male   Alabama       0.0239
6  1990 10 to 14 years Other Female Alabama       0.0235
# A tibble: 6 x 6
  STATE   SEX   RACE  AGE_GROUP      YEAR PERC_SUB_POP
  <chr>   <chr> <chr> <chr>         <dbl>        <dbl>
1 Alabama Male  White Under 5 years  2000         2.24
2 Alabama Male  White Under 5 years  2001         2.24
3 Alabama Male  White Under 5 years  2002         2.22
4 Alabama Male  White Under 5 years  2003         2.21
5 Alabama Male  White Under 5 years  2004         2.20
6 Alabama Male  White Under 5 years  2005         2.20
[1] 18
[1] 18
[1] 18
[1] 18
# A tibble: 1 x 1
  years_data
       <int>
1         34
[1] 34
DONOHUE_AGE_GROUPS <- c("15 to 19 years",
                        "20 to 24 years",
                        "25 to 29 years",
                        "30 to 34 years",
                        "35 to 39 years")

DONOHUE_RACE <- c("White",
                  "Black",
                  "Other")

DONOHUE_SEX <- c("Male")

dem_DONOHUE <- dem %>%
  filter(AGE_GROUP %in% DONOHUE_AGE_GROUPS,
         RACE %in% DONOHUE_RACE,
         SEX %in% DONOHUE_SEX) %>%
  mutate(AGE_GROUP = fct_collapse(AGE_GROUP, "20 to 39 years"=c("20 to 24 years",
                                                                "25 to 29 years",
                                                                "30 to 34 years",
                                                                "35 to 39 years"))) %>%
  mutate(AGE_GROUP = str_replace_all(AGE_GROUP," ","_")) %>%
  group_by(YEAR, STATE, RACE, SEX, AGE_GROUP) %>%
  summarise(PERC_SUB_POP = sum(PERC_SUB_POP), .groups = "drop") %>%
  unite(col = "VARIABLE", RACE, SEX, AGE_GROUP, sep = "_") %>%
  rename("VALUE"=PERC_SUB_POP)

LOTT_AGE_GROUPS_NULL <- c("Under 5 years",
                          "5 to 9 years")

LOTT_RACE <- c("White",
               "Black",
               "Other")

LOTT_SEX <- c("Male",
              "Female")

dem_LOTT <- dem %>%
  filter(!(AGE_GROUP %in% LOTT_AGE_GROUPS_NULL),
         RACE %in% LOTT_RACE,
         SEX %in% LOTT_SEX) %>%
  mutate(AGE_GROUP = fct_collapse(AGE_GROUP,
                                  "10 to 19 years"=c("10 to 14 years",
                                                     "15 to 19 years"),
                                  "20 to 29 years"=c("20 to 24 years",
                                                     "25 to 29 years"),
                                  "30 to 39 years"=c("30 to 34 years",
                                                     "35 to 39 years"),
                                  "40 to 49 years"=c("40 to 44 years",
                                                     "45 to 49 years"),
                                  "50 to 64 years"=c("50 to 54 years",
                                                     "55 to 59 years",
                                                     "60 to 64 years"),
                                  "65 years and over"=c("65 to 69 years",
                                                        "70 to 74 years",
                                                        "75 to 79 years",
                                                        "80 to 84 years",
                                                        "85 years and over"))) %>%
  mutate(AGE_GROUP = str_replace_all(AGE_GROUP," ","_")) %>%
  group_by(YEAR, STATE, RACE, SEX, AGE_GROUP) %>%
  summarise(PERC_SUB_POP = sum(PERC_SUB_POP), .groups = "drop") %>%
  unite(col = "VARIABLE", RACE, SEX, AGE_GROUP, sep = "_") %>%
  rename("VALUE"=PERC_SUB_POP)
  
dim(expand.grid(c(1:6), c(7:8), c(9:10)))[1]
[1] 24
[1] TRUE
[1] TRUE
[1] TRUE
# A tibble: 6 x 3
   YEAR STATE       TOT_POP
  <dbl> <chr>         <dbl>
1  1977 Alabama     3782571
2  1977 Alaska       397220
3  1977 Arizona     2427296
4  1977 Arkansas    2207195
5  1977 California 22350332
6  1977 Colorado    2696179
# A tibble: 6 x 3
   YEAR STATE       TOT_POP
  <dbl> <chr>         <dbl>
1  1980 Alabama     3899671
2  1980 Alaska       404680
3  1980 Arizona     2735840
4  1980 Arkansas    2288809
5  1980 California 23792840
6  1980 Colorado    2909545
# A tibble: 6 x 3
   YEAR STATE       TOT_POP
  <dbl> <chr>         <dbl>
1  1990 Alabama     4048508
2  1990 Alaska       553120
3  1990 Arizona     3679056
4  1990 Arkansas    2354343
5  1990 California 29950111
6  1990 Colorado    3303862
# A tibble: 6 x 3
   YEAR STATE       TOT_POP
  <dbl> <chr>         <dbl>
1  2000 Alabama     4452173
2  2000 Alaska       627963
3  2000 Arizona     5160586
4  2000 Arkansas    2678588
5  2000 California 33987977
6  2000 Colorado    4326921
# A tibble: 34 x 2
    YEAR     n
   <dbl> <int>
 1  1977    51
 2  1978    51
 3  1979    51
 4  1980    51
 5  1981    51
 6  1982    51
 7  1983    51
 8  1984    51
 9  1985    51
10  1986    51
11  1987    51
12  1988    51
13  1989    51
14  1990    51
15  1991    51
16  1992    51
17  1993    51
18  1994    51
19  1995    51
20  1996    51
21  1997    51
22  1998    51
23  1999    51
24  2000    51
25  2001    51
26  2002    51
27  2003    51
28  2004    51
29  2005    51
30  2006    51
31  2007    51
32  2008    51
33  2009    51
34  2010    51

Police staffing

 [1] "data_year"             "ori"                   "pub_agency_name"      
 [4] "pub_agency_unit"       "state_abbr"            "division_name"        
 [7] "region_name"           "county_name"           "agency_type_name"     
[10] "population_group_desc" "population"            "male_officer_ct"      
[13] "male_civilian_ct"      "male_total_ct"         "female_officer_ct"    
[16] "female_civilian_ct"    "female_total_ct"       "officer_ct"           
[19] "civilian_ct"           "total_pe_ct"           "pe_ct_per_1000"       
# A tibble: 59 x 2
   state_abbr     n
   <chr>      <int>
 1 AK            38
 2 AL            38
 3 AR            38
 4 AS            38
 5 AZ            38
 6 CA            38
 7 CO            38
 8 CT            38
 9 CZ            38
10 DC            38
11 DE            38
12 FL            38
13 FS            38
14 GA            38
15 GM            38
16 HI            38
17 IA            38
18 ID            38
19 IL            38
20 IN            38
21 KS            38
22 KY            38
23 LA            38
24 MA            38
25 MD            38
26 ME            38
27 MI            38
28 MN            38
29 MO            38
30 MP            38
31 MS            38
32 MT            38
33 NB            38
34 NC            38
35 ND            38
36 NH            38
37 NJ            38
38 NM            38
39 NV            38
40 NY            38
41 OH            38
42 OK            38
43 OR            38
44 OT            38
45 PA            38
46 PR            38
47 RI            38
48 SC            38
49 SD            38
50 TN            38
51 TX            38
52 UT            38
53 VA            38
54 VI            38
55 VT            38
56 WA            38
57 WI            38
58 WV            38
59 WY            38
   state_abbr          STATE
1          AL        Alabama
2          AK         Alaska
3          AZ        Arizona
4          AR       Arkansas
5          CA     California
6          CO       Colorado
7          CT    Connecticut
8          DE       Delaware
9          FL        Florida
10         GA        Georgia
11         HI         Hawaii
12         ID          Idaho
13         IL       Illinois
14         IN        Indiana
15         IA           Iowa
16         KS         Kansas
17         KY       Kentucky
18         LA      Louisiana
19         ME          Maine
20         MD       Maryland
21         MA  Massachusetts
22         MI       Michigan
23         MN      Minnesota
24         MS    Mississippi
25         MO       Missouri
26         MT        Montana
27         NE       Nebraska
28         NV         Nevada
29         NH  New Hampshire
30         NJ     New Jersey
31         NM     New Mexico
32         NY       New York
33         NC North Carolina
34         ND   North Dakota
35         OH           Ohio
36         OK       Oklahoma
37         OR         Oregon
38         PA   Pennsylvania
39         RI   Rhode Island
40         SC South Carolina
41         SD   South Dakota
42         TN      Tennessee
43         TX          Texas
44         UT           Utah
45         VT        Vermont
46         VA       Virginia
47         WA     Washington
48         WV  West Virginia
49         WI      Wisconsin
50         WY        Wyoming

Unemployment

# A tibble: 1 x 14
   Year   Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  2020   3.2   2.9     3  13.3    NA    NA    NA    NA    NA    NA    NA    NA
# … with 1 more variable: Annual <dbl>
 [1] "STATE"  "Year"   "Jan"    "Feb"    "Mar"    "Apr"    "May"    "Jun"   
 [9] "Jul"    "Aug"    "Sep"    "Oct"    "Nov"    "Dec"    "Annual"
      STATE        Year         Jan         Feb         Mar         Apr 
"character"   "numeric"   "numeric"   "numeric"   "numeric"   "numeric" 
        May         Jun         Jul         Aug         Sep         Oct 
  "numeric"   "numeric"   "numeric"   "numeric"   "numeric"   "numeric" 
        Nov         Dec      Annual 
  "numeric"   "numeric"   "numeric" 

Poverty rate

# A tibble: 6 x 6
  `NOTE: Number in thousa… ...2  ...3   ...4         ...5         ...6          
  <chr>                    <chr> <chr>  <chr>        <chr>        <chr>         
1 2018                     <NA>  <NA>    <NA>        <NA>          <NA>         
2 STATE                    Total Number "Standard\n… Percent      "Standard\ner…
3 Alabama                  4877  779    "65"         16           "1.3"         
4 Alaska                   720   94     "9"          13.1         "1.2"         
5 Arizona                  7241  929    "80"         12.80000000… "1.1000000000…
6 Arkansas                 2912  462    "38"         15.9         "1.3"         
# A tibble: 6 x 6
  STATE                             Total Number Number_se Percent Percent_se   
  <chr>                             <chr> <chr>  <chr>     <chr>   <chr>        
1 Wisconsin                         4724  403    57        8.5     1.1000000000…
2 Wyoming                           468   49     20        10.4    4            
3 Standard errors shown in this ta… <NA>  <NA>   <NA>      <NA>    <NA>         
4 For information on confidentiali… <NA>  <NA>   <NA>      <NA>    <NA>         
5 Footnotes are available at <www.… <NA>  <NA>   <NA>      <NA>    <NA>         
6 SOURCE: U.S. Bureau of the Censu… <NA>  <NA>   <NA>      <NA>    <NA>         
[1] "2 extra groups"
# A tibble: 6 x 7
  STATE   Total Number Number_se      Percent        Percent_se       year_group
  <chr>   <chr> <chr>  <chr>          <chr>          <chr>                 <int>
1 2018    <NA>  <NA>    <NA>          <NA>            <NA>                     1
2 STATE   Total Number "Standard\ner… Percent        "Standard\nerro…          1
3 Alabama 4877  779    "65"           16             "1.3"                     1
4 Alaska  720   94     "9"            13.1           "1.2"                     1
5 Arizona 7241  929    "80"           12.8000000000… "1.100000000000…          1
6 Arkans… 2912  462    "38"           15.9           "1.3"                     1
# A tibble: 2 x 2
   n_na     n
  <dbl> <int>
1     0  1989
2     5    39
       YEAR       STATE       Total      Number   Number_se     Percent 
"character" "character" "character" "character" "character" "character" 
 Percent_se        n_na 
"character"   "numeric" 
[1] "YEAR"     "STATE"    "VALUE"    "VARIABLE"

Violent crime

https://www.ucrdatatool.gov/Search/Crime/State/StatebyState.cfm

[1] 2254
       YEAR          VC       STATE 
"character" "character" "character" 

RTC laws

 [1] 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109
[20] 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109 109
[39] 109 109 109 109 109 109 109 109 109 109 109 109 109 109  63
[1] "                                                             60"
 [1] 3 4 4 4 3 4 2 3 2 4 4 3 4 3 4 4 4 4 4 4 3 2 4 4 4 4 4 4 4 2 3 3 3 3 3 4 4 4
[39] 3 3 2 3 3 4 4 4 3 4 2 3 4 4
 [1] 2 3 3 3 2 3 2 2 2 3 3 2 3 2 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 2 2 3 2 3 3 3 3 3
[39] 3 3 2 3 3 3 3 3 2 3 2 3 3 3
 [1] 2 2 2 2 1 2 1 1 1 2 2 2 2 1 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2
[39] 2 2 1 2 2 2 2 2 2 2 1 2 2 2
 [1] 1 0 0 0 1 0 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0
[39] 0 0 1 0 0 0 0 0 1 0 1 0 0 0
                                                                                                              .
1       Alabama                    1975                                                                    1975
2        Alaska                 10/1/1994                          0.252                                   1995
3        Arizona                7/17/1994                          0.460                                   1995
4       Arkansas                7/27/1995                          0.433                                   1996
5      California                  N/A                                                                        0
6       Colorado                5/17/2003                          0.627                                   2003
  apply(p_62, 1, str_count, "\\\\s{40,}")
1                                       1
2                                       0
3                                       0
4                                       0
5                                       1
6                                       0
$`|Alabama||1975|N/A|1975`
[1] 0 7 4 3 4

$`|Alaska||10/1/1994||0.252|||1995`
[1] 0 6 9 5 4

$`|Arizona| 7/17/1994||0.460|||1995`
[1]  0  7 10  5  4

$`|Arkansas| 7/27/1995||0.433|||1996`
[1]  0  8 10  5  4

$`|California||N/A|N/A|0`
[1]  0 10  3  3  1

$`|Colorado| 5/17/2003||0.627|||2003`
[1]  0  8 10  5  4

$`|Connecticut||1970|N/A|1970`
[1]  0 11  4  3  4

$`|Delaware||N/A|N/A|0`
[1] 0 8 3 3 1

$`District of Columbia|N/A|N/A|0`
[1] 20  3  3  1

$`|Florida| 10/1/1987||0.252|||1988`
[1]  0  7 10  5  4

$`|Georgia| 8/25/1989||0.353|||1990`
[1]  0  7 10  5  4

$`|Hawaii||N/A|N/A|0`
[1] 0 6 3 3 1

$`|Idaho||7/1/1990||0.504|||1990`
[1] 0 5 8 5 4

$`|Illinois| 1/5/2014|N/A|2014`
[1] 0 8 9 3 4

$`|Indiana| 1/15/1980||0.962|||1980`
[1]  0  7 10  5  4

$`|Iowa||1/1/2011||1.000|||2011`
[1] 0 4 8 5 4

$`|Kansas||1/1/2007||1.000|||2007`
[1] 0 6 8 5 4

$`|Kentucky| 10/1/1996||0.251|||1997`
[1]  0  8 10  5  4

$`|Louisiana|4/19/1996||0.702|||1996`
[1] 0 9 9 5 4

$`|Maine||9/19/1985||0.285|||1986`
[1] 0 5 9 5 4

$`|Maryland||N/A|N/A|0`
[1] 0 8 3 3 1

$`|Massachusetts||N/A|N/A|0`
[1]  0 13  3  3  1

$`|Michigan||7/1/2001||0.504|||2001`
[1] 0 8 8 5 4

$`|Minnesota| 5/28/2003||0.597|||2003`
[1]  0  9 10  5  4

$`|Mississippi|7/1/1990||0.504|||1990`
[1]  0 11  8  5  4

$`|Missouri| 2/26/2004||0.847|||2004`
[1]  0  8 10  5  4

$`|Montana||10/1/1991||0.252|||1992`
[1] 0 7 9 5 4

$`|Nebraska||1/1/2007||1.000|||2007`
[1] 0 8 8 5 4

$`|Nevada||10/1/1995||0.252|||1996`
[1] 0 6 9 5 4

$`|New Hampshire||1959|N/A|1959`
[1]  0 13  4  3  4

$`|New Jersey||N/A|N/A|0`
[1]  0 10  3  3  1

$`|New Mexico||1/1/2004||1.000|||2004`
[1]  0 10  8  5  4

$`|New York||N/A|N/A|0`
[1] 0 8 3 3 1

$`|North Carolina|12/1/1995||0.085|||1996`
[1]  0 14  9  5  4

$`|North Dakota| 8/1/1985||0.419|||1986`
[1]  0 12  9  5  4

$`|Ohio||4/8/2004||0.732|||2004`
[1] 0 4 8 5 4

$`|Oklahoma||1/1/1996||1.000|||1996`
[1] 0 8 8 5 4

$`|Oregon||1/1/1990||1.000|||1990`
[1] 0 6 8 5 4

$`|Pennsylvania|6/17/1989||0.542|||1989`
[1]  0 12  9  5  4

$`|Philadelphia|10/11/1995||0.225|||1996`
[1]  0 12 10  5  4

$`|Rhode Island||N/A|N/A|0`
[1]  0 12  3  3  1

$`|South Carolina|8/23/1996||0.358|||1997`
[1]  0 14  9  5  4

$`|South Dakota| 7/1/1985||0.504|||1985`
[1]  0 12  9  5  4

$`|Tennessee| 10/1/1996||0.251|||1997`
[1]  0  9 10  5  4

$`|Texas||1/1/1996||1.000|||1996`
[1] 0 5 8 5 4

$`|Utah||5/1/1995||0.671|||1995`
[1] 0 4 8 5 4

$`|Vermont||1970|N/A|1970`
[1] 0 7 4 3 4

$`|Virginia| 5/5/1995||0.660|||1995`
[1] 0 8 9 5 4

$`|Washington||1961|N/A|1961`
[1]  0 10  4  3  4

$`|West Virginia|7/7/1989||0.488|||1990`
[1]  0 13  8  5  4

$`|Wisconsin| 11/1/2011||0.167|||2012`
[1]  0  9 10  5  4

$`|Wyoming||10/1/1994||0.252|||1995`
[1] 0 7 9 5 4
 [1] "|Alabama||1975|N/A|1975"                
 [2] "|Alaska||10/1/1994||0.252|||1995"       
 [3] "|Arizona| 7/17/1994||0.460|||1995"      
 [4] "|Arkansas| 7/27/1995||0.433|||1996"     
 [5] "|California||N/A|N/A|0"                 
 [6] "|Colorado| 5/17/2003||0.627|||2003"     
 [7] "|Connecticut||1970|N/A|1970"            
 [8] "|Delaware||N/A|N/A|0"                   
 [9] "District of Columbia|N/A|N/A|0"         
[10] "|Florida| 10/1/1987||0.252|||1988"      
[11] "|Georgia| 8/25/1989||0.353|||1990"      
[12] "|Hawaii||N/A|N/A|0"                     
[13] "|Idaho||7/1/1990||0.504|||1990"         
[14] "|Illinois| 1/5/2014|N/A|2014"           
[15] "|Indiana| 1/15/1980||0.962|||1980"      
[16] "|Iowa||1/1/2011||1.000|||2011"          
[17] "|Kansas||1/1/2007||1.000|||2007"        
[18] "|Kentucky| 10/1/1996||0.251|||1997"     
[19] "|Louisiana|4/19/1996||0.702|||1996"     
[20] "|Maine||9/19/1985||0.285|||1986"        
[21] "|Maryland||N/A|N/A|0"                   
[22] "|Massachusetts||N/A|N/A|0"              
[23] "|Michigan||7/1/2001||0.504|||2001"      
[24] "|Minnesota| 5/28/2003||0.597|||2003"    
[25] "|Mississippi|7/1/1990||0.504|||1990"    
[26] "|Missouri| 2/26/2004||0.847|||2004"     
[27] "|Montana||10/1/1991||0.252|||1992"      
[28] "|Nebraska||1/1/2007||1.000|||2007"      
[29] "|Nevada||10/1/1995||0.252|||1996"       
[30] "|New Hampshire||1959|N/A|1959"          
[31] "|New Jersey||N/A|N/A|0"                 
[32] "|New Mexico||1/1/2004||1.000|||2004"    
[33] "|New York||N/A|N/A|0"                   
[34] "|North Carolina|12/1/1995||0.085|||1996"
[35] "|North Dakota| 8/1/1985||0.419|||1986"  
[36] "|Ohio||4/8/2004||0.732|||2004"          
[37] "|Oklahoma||1/1/1996||1.000|||1996"      
[38] "|Oregon||1/1/1990||1.000|||1990"        
[39] "|Pennsylvania|6/17/1989||0.542|||1989"  
[40] "|Philadelphia|10/11/1995||0.225|||1996" 
[41] "|Rhode Island||N/A|N/A|0"               
[42] "|South Carolina|8/23/1996||0.358|||1997"
[43] "|South Dakota| 7/1/1985||0.504|||1985"  
[44] "|Tennessee| 10/1/1996||0.251|||1997"    
[45] "|Texas||1/1/1996||1.000|||1996"         
[46] "|Utah||5/1/1995||0.671|||1995"          
[47] "|Vermont||1970|N/A|1970"                
[48] "|Virginia| 5/5/1995||0.660|||1995"      
[49] "|Washington||1961|N/A|1961"             
[50] "|West Virginia|7/7/1989||0.488|||1990"  
[51] "|Wisconsin| 11/1/2011||0.167|||2012"    
[52] "|Wyoming||10/1/1994||0.252|||1995"      
              STATE          E_Date_RTC Frac_Yr_Eff_Yr_Pass         RTC_Date_SA 
        "character"         "character"         "character"         "character" 
       STATE RTC_LAW_YEAR 
 "character"    "numeric" 
       STATE RTC_LAW_YEAR
1    Alabama         1975
2     Alaska         1995
3    Arizona         1995
4   Arkansas         1996
5 California          Inf
6   Colorado         2003

Checkpoint

[1] "YEAR"     "STATE"    "VARIABLE" "VALUE"   
[1] "YEAR"     "STATE"    "VARIABLE" "VALUE"   
[1] "STATE"    "YEAR"     "VALUE"    "VARIABLE"
[1] "YEAR"     "STATE"    "VALUE"    "VARIABLE"
[1] "YEAR"     "VALUE"    "STATE"    "VARIABLE"
# A tibble: 6 x 4
   YEAR STATE   VARIABLE                    VALUE
  <dbl> <chr>   <chr>                       <dbl>
1  1990 Alabama Black_Male_15_to_19_years  1.23  
2  1990 Alabama Black_Male_20_to_39_years  3.62  
3  1990 Alabama Other_Male_15_to_19_years  0.0470
4  1990 Alabama Other_Male_20_to_39_years  0.176 
5  1990 Alabama White_Male_15_to_19_years  2.70  
6  1990 Alabama White_Male_20_to_39_years 11.6   
# A tibble: 6 x 4
   YEAR STATE   VARIABLE                       VALUE
  <dbl> <chr>   <chr>                          <dbl>
1  1990 Alabama Black_Female_10_to_19_years     2.45
2  1990 Alabama Black_Female_20_to_29_years     2.20
3  1990 Alabama Black_Female_30_to_39_years     2.18
4  1990 Alabama Black_Female_40_to_49_years     1.37
5  1990 Alabama Black_Female_50_to_64_years     1.53
6  1990 Alabama Black_Female_65_years_and_over  1.67
# A tibble: 6 x 4
  STATE    YEAR VALUE VARIABLE         
  <chr>   <dbl> <dbl> <chr>            
1 Alabama  1977   7.3 Unemployment_rate
2 Alabama  1978   6.4 Unemployment_rate
3 Alabama  1979   7.2 Unemployment_rate
4 Alabama  1980   8.9 Unemployment_rate
5 Alabama  1981  10.6 Unemployment_rate
6 Alabama  1982  14.1 Unemployment_rate
# A tibble: 6 x 4
   YEAR STATE      VALUE VARIABLE    
  <dbl> <chr>      <dbl> <chr>       
1  2018 Alabama     16   Poverty_rate
2  2018 Alaska      13.1 Poverty_rate
3  2018 Arizona     12.8 Poverty_rate
4  2018 Arkansas    15.9 Poverty_rate
5  2018 California  11.9 Poverty_rate
6  2018 Colorado     9.1 Poverty_rate
# A tibble: 6 x 4
   YEAR VALUE STATE   VARIABLE        
  <dbl> <dbl> <chr>   <chr>           
1  1977 15293 Alabama Viol_crime_count
2  1978 15682 Alabama Viol_crime_count
3  1979 15578 Alabama Viol_crime_count
4  1980 17320 Alabama Viol_crime_count
5  1981 18423 Alabama Viol_crime_count
6  1982 17653 Alabama Viol_crime_count

Join

Donohue, et al.

# A tibble: 33 x 2
    YEAR     n
   <dbl> <int>
 1  1980    52
 2  1981    52
 3  1982    52
 4  1983    52
 5  1984    52
 6  1985    52
 7  1986    52
 8  1987    52
 9  1988    52
10  1989    52
11  1990    52
12  1991    52
13  1992    52
14  1993    52
15  1994    52
16  1995    52
17  1996    52
18  1997    52
19  1998    52
20  1999    52
21  2000    52
22  2001    52
23  2002    52
24  2003    52
25  2004    52
26  2005    52
27  2006    52
28  2007    52
29  2008    52
30  2009    52
31  2010    52
32  2011    52
33  2012    52
             Alabama               Alaska              Arizona 
                  44                   44                   44 
            Arkansas           California             Colorado 
                  44                   44                   44 
         Connecticut                 D.C.             Delaware 
                  44                   33                   44 
District of Columbia              Florida              Georgia 
                  44                   44                   44 
              Hawaii                Idaho             Illinois 
                  44                   44                   44 
             Indiana                 Iowa               Kansas 
                  44                   44                   44 
            Kentucky            Louisiana                Maine 
                  44                   44                   44 
            Maryland        Massachusetts             Michigan 
                  44                   44                   44 
           Minnesota          Mississippi             Missouri 
                  44                   44                   44 
             Montana             Nebraska               Nevada 
                  44                   44                   44 
       New Hampshire           New Jersey           New Mexico 
                  44                   44                   44 
            New York       North Carolina         North Dakota 
                  44                   44                   44 
                Ohio             Oklahoma               Oregon 
                  44                   44                   44 
        Pennsylvania         Rhode Island       South Carolina 
                  44                   44                   44 
        South Dakota            Tennessee                Texas 
                  44                   44                   44 
                Utah              Vermont             Virginia 
                  44                   44                   44 
          Washington        West Virginia            Wisconsin 
                  44                   44                   44 
             Wyoming 
                  44 
[1] 44
             Alabama               Alaska              Arizona 
                  44                   44                   44 
            Arkansas           California             Colorado 
                  44                   44                   44 
         Connecticut District of Columbia             Delaware 
                  44                   77                   44 
             Florida              Georgia               Hawaii 
                  44                   44                   44 
               Idaho             Illinois              Indiana 
                  44                   44                   44 
                Iowa               Kansas             Kentucky 
                  44                   44                   44 
           Louisiana                Maine             Maryland 
                  44                   44                   44 
       Massachusetts             Michigan            Minnesota 
                  44                   44                   44 
         Mississippi             Missouri              Montana 
                  44                   44                   44 
            Nebraska               Nevada        New Hampshire 
                  44                   44                   44 
          New Jersey           New Mexico             New York 
                  44                   44                   44 
      North Carolina         North Dakota                 Ohio 
                  44                   44                   44 
            Oklahoma               Oregon         Pennsylvania 
                  44                   44                   44 
        Rhode Island       South Carolina         South Dakota 
                  44                   44                   44 
           Tennessee                Texas                 Utah 
                  44                   44                   44 
             Vermont             Virginia           Washington 
                  44                   44                   44 
       West Virginia            Wisconsin              Wyoming 
                  44                   44                   44 
[1] 51
             Alabama               Alaska              Arizona 
                  21                   21                   21 
            Arkansas           California             Colorado 
                  21                   21                   21 
         Connecticut District of Columbia             Delaware 
                  21                   21                   21 
             Florida              Georgia               Hawaii 
                  21                   21                   21 
               Idaho             Illinois              Indiana 
                  21                   21                   21 
                Iowa               Kansas             Kentucky 
                  21                   21                   21 
           Louisiana                Maine             Maryland 
                  21                   21                   21 
       Massachusetts             Michigan            Minnesota 
                  21                   21                   21 
         Mississippi             Missouri              Montana 
                  21                   21                   21 
            Nebraska               Nevada        New Hampshire 
                  21                   21                   21 
          New Jersey           New Mexico             New York 
                  21                   21                   21 
      North Carolina         North Dakota                 Ohio 
                  21                   21                   21 
            Oklahoma               Oregon         Pennsylvania 
                  21                   21                   21 
        Rhode Island       South Carolina         South Dakota 
                  21                   21                   21 
           Tennessee                Texas                 Utah 
                  21                   21                   21 
             Vermont             Virginia           Washington 
                  21                   21                   21 
       West Virginia            Wisconsin              Wyoming 
                  21                   21                   21 
              Alaska              Arizona             Arkansas 
                  21                   21                   21 
          California             Colorado District of Columbia 
                  21                   21                   21 
            Delaware               Hawaii             Illinois 
                  21                   21                   21 
                Iowa               Kansas             Kentucky 
                  21                   21                   21 
           Louisiana             Maryland        Massachusetts 
                  21                   21                   21 
            Michigan            Minnesota             Missouri 
                  21                   21                   21 
             Montana             Nebraska               Nevada 
                  21                   21                   21 
          New Jersey           New Mexico             New York 
                  21                   21                   21 
      North Carolina                 Ohio             Oklahoma 
                  21                   21                   21 
        Rhode Island       South Carolina            Tennessee 
                  21                   21                   21 
               Texas                 Utah             Virginia 
                  21                   21                   21 
           Wisconsin              Wyoming 
                  21                   21 
[1] 35

Lott and Mustard

# A tibble: 33 x 2
    YEAR     n
   <dbl> <int>
 1  1980    52
 2  1981    52
 3  1982    52
 4  1983    52
 5  1984    52
 6  1985    52
 7  1986    52
 8  1987    52
 9  1988    52
10  1989    52
11  1990    52
12  1991    52
13  1992    52
14  1993    52
15  1994    52
16  1995    52
17  1996    52
18  1997    52
19  1998    52
20  1999    52
21  2000    52
22  2001    52
23  2002    52
24  2003    52
25  2004    52
26  2005    52
27  2006    52
28  2007    52
29  2008    52
30  2009    52
31  2010    52
32  2011    52
33  2012    52
             Alabama               Alaska              Arizona 
                  44                   44                   44 
            Arkansas           California             Colorado 
                  44                   44                   44 
         Connecticut                 D.C.             Delaware 
                  44                   33                   44 
District of Columbia              Florida              Georgia 
                  44                   44                   44 
              Hawaii                Idaho             Illinois 
                  44                   44                   44 
             Indiana                 Iowa               Kansas 
                  44                   44                   44 
            Kentucky            Louisiana                Maine 
                  44                   44                   44 
            Maryland        Massachusetts             Michigan 
                  44                   44                   44 
           Minnesota          Mississippi             Missouri 
                  44                   44                   44 
             Montana             Nebraska               Nevada 
                  44                   44                   44 
       New Hampshire           New Jersey           New Mexico 
                  44                   44                   44 
            New York       North Carolina         North Dakota 
                  44                   44                   44 
                Ohio             Oklahoma               Oregon 
                  44                   44                   44 
        Pennsylvania         Rhode Island       South Carolina 
                  44                   44                   44 
        South Dakota            Tennessee                Texas 
                  44                   44                   44 
                Utah              Vermont             Virginia 
                  44                   44                   44 
          Washington        West Virginia            Wisconsin 
                  44                   44                   44 
             Wyoming 
                  44 
[1] 44
             Alabama               Alaska              Arizona 
                  44                   44                   44 
            Arkansas           California             Colorado 
                  44                   44                   44 
         Connecticut District of Columbia             Delaware 
                  44                   77                   44 
             Florida              Georgia               Hawaii 
                  44                   44                   44 
               Idaho             Illinois              Indiana 
                  44                   44                   44 
                Iowa               Kansas             Kentucky 
                  44                   44                   44 
           Louisiana                Maine             Maryland 
                  44                   44                   44 
       Massachusetts             Michigan            Minnesota 
                  44                   44                   44 
         Mississippi             Missouri              Montana 
                  44                   44                   44 
            Nebraska               Nevada        New Hampshire 
                  44                   44                   44 
          New Jersey           New Mexico             New York 
                  44                   44                   44 
      North Carolina         North Dakota                 Ohio 
                  44                   44                   44 
            Oklahoma               Oregon         Pennsylvania 
                  44                   44                   44 
        Rhode Island       South Carolina         South Dakota 
                  44                   44                   44 
           Tennessee                Texas                 Utah 
                  44                   44                   44 
             Vermont             Virginia           Washington 
                  44                   44                   44 
       West Virginia            Wisconsin              Wyoming 
                  44                   44                   44 
[1] 51
             Alabama               Alaska              Arizona 
                  21                   21                   21 
            Arkansas           California             Colorado 
                  21                   21                   21 
         Connecticut District of Columbia             Delaware 
                  21                   21                   21 
             Florida              Georgia               Hawaii 
                  21                   21                   21 
               Idaho             Illinois              Indiana 
                  21                   21                   21 
                Iowa               Kansas             Kentucky 
                  21                   21                   21 
           Louisiana                Maine             Maryland 
                  21                   21                   21 
       Massachusetts             Michigan            Minnesota 
                  21                   21                   21 
         Mississippi             Missouri              Montana 
                  21                   21                   21 
            Nebraska               Nevada        New Hampshire 
                  21                   21                   21 
          New Jersey           New Mexico             New York 
                  21                   21                   21 
      North Carolina         North Dakota                 Ohio 
                  21                   21                   21 
            Oklahoma               Oregon         Pennsylvania 
                  21                   21                   21 
        Rhode Island       South Carolina         South Dakota 
                  21                   21                   21 
           Tennessee                Texas                 Utah 
                  21                   21                   21 
             Vermont             Virginia           Washington 
                  21                   21                   21 
       West Virginia            Wisconsin              Wyoming 
                  21                   21                   21 
              Alaska              Arizona             Arkansas 
                  21                   21                   21 
          California             Colorado District of Columbia 
                  21                   21                   21 
            Delaware               Hawaii             Illinois 
                  21                   21                   21 
                Iowa               Kansas             Kentucky 
                  21                   21                   21 
           Louisiana             Maryland        Massachusetts 
                  21                   21                   21 
            Michigan            Minnesota             Missouri 
                  21                   21                   21 
             Montana             Nebraska               Nevada 
                  21                   21                   21 
          New Jersey           New Mexico             New York 
                  21                   21                   21 
      North Carolina                 Ohio             Oklahoma 
                  21                   21                   21 
        Rhode Island       South Carolina            Tennessee 
                  21                   21                   21 
               Texas                 Utah             Virginia 
                  21                   21                   21 
           Wisconsin              Wyoming 
                  21                   21 
[1] 35

Data Exploration


                    STATE                      YEAR Black_Male_15_to_19_years 
                 "factor"                 "numeric"                 "numeric" 
Black_Male_20_to_39_years Other_Male_15_to_19_years Other_Male_20_to_39_years 
                "numeric"                 "numeric"                 "numeric" 
White_Male_15_to_19_years White_Male_20_to_39_years         Unemployment_rate 
                "numeric"                 "numeric"                 "numeric" 
             Poverty_rate          Viol_crime_count                Population 
                "numeric"                 "numeric"                 "numeric" 
      police_per_100k_lag              RTC_LAW_YEAR                   RTC_LAW 
                "numeric"                 "numeric"                 "logical" 
                   TIME_0                  TIME_INF        Viol_crime_rate_1k 
                "numeric"                 "numeric"                 "numeric" 
   Viol_crime_rate_1k_log            Population_log 
                "numeric"                 "numeric" 

Data Analysis


Donohue, et al.

Some code taken from http://karthur.org/2019/implementing-fixed-effects-panel-models-in-r.html

Twoways effects Within Model

Call:
plm(formula = Viol_crime_rate_1k_log ~ RTC_LAW + White_Male_15_to_19_years + 
    White_Male_20_to_39_years + Black_Male_15_to_19_years + Black_Male_20_to_39_years + 
    Other_Male_15_to_19_years + Other_Male_20_to_39_years + Unemployment_rate + 
    Poverty_rate + Population_log + police_per_100k_lag, data = d_panel_DONOHUE, 
    effect = "twoways", model = "within")

Balanced Panel: n = 35, T = 21, N = 735

Residuals:
      Min.    1st Qu.     Median    3rd Qu.       Max. 
-0.6338573 -0.0622728 -0.0032734  0.0600340  0.4747461 

Coefficients:
                             Estimate  Std. Error t-value  Pr(>|t|)    
RTC_LAWTRUE                0.01399747  0.01807103  0.7746 0.4388610    
White_Male_15_to_19_years -0.07163612  0.04256304 -1.6831 0.0928302 .  
White_Male_20_to_39_years  0.04735001  0.01368064  3.4611 0.0005722 ***
Black_Male_15_to_19_years  0.14345532  0.08877101  1.6160 0.1065624    
Black_Male_20_to_39_years  0.13841529  0.02430620  5.6946 1.852e-08 ***
Other_Male_15_to_19_years  0.85500263  0.11590669  7.3766 4.822e-13 ***
Other_Male_20_to_39_years -0.25713549  0.04257069 -6.0402 2.554e-09 ***
Unemployment_rate          0.00221657  0.00654484  0.3387 0.7349615    
Poverty_rate               0.00392491  0.00332665  1.1798 0.2384832    
Population_log             0.23504152  0.09612692  2.4451 0.0147374 *  
police_per_100k_lag        0.00054079  0.00017321  3.1222 0.0018723 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Total Sum of Squares:    12.935
Residual Sum of Squares: 10.268
R-Squared:      0.20621
Adj. R-Squared: 0.12909
F-statistic: 15.7996 on 11 and 669 DF, p-value: < 2.22e-16

Lott and Mustard

Some code taken from http://karthur.org/2019/implementing-fixed-effects-panel-models-in-r.html

Twoways effects Within Model

Call:
plm(formula = LOTT_fmla, data = d_panel_LOTT, effect = "twoways", 
    model = "within")

Balanced Panel: n = 35, T = 21, N = 735

Residuals:
       Min.     1st Qu.      Median     3rd Qu.        Max. 
-0.46717726 -0.05325358  0.00074259  0.05557220  0.31537243 

Coefficients:
                                  Estimate  Std. Error t-value  Pr(>|t|)    
RTC_LAWTRUE                    -0.02936965  0.01823854 -1.6103 0.1078246    
White_Female_10_to_19_years     0.65921612  0.20489684  3.2173 0.0013594 ** 
White_Female_20_to_29_years     0.08644467  0.07612062  1.1356 0.2565382    
White_Female_30_to_39_years    -0.13836498  0.10773830 -1.2843 0.1995132    
White_Female_40_to_49_years     0.47203824  0.10700674  4.4113 1.206e-05 ***
White_Female_50_to_64_years    -0.43678487  0.09390272 -4.6515 4.007e-06 ***
White_Female_65_years_and_over -0.28217891  0.08930520 -3.1597 0.0016538 ** 
White_Male_10_to_19_years      -0.58796604  0.19526018 -3.0112 0.0027049 ** 
White_Male_20_to_29_years       0.02921390  0.07057479  0.4139 0.6790551    
White_Male_30_to_39_years       0.07899878  0.10478693  0.7539 0.4511875    
White_Male_40_to_49_years      -0.44302519  0.09301896 -4.7627 2.365e-06 ***
White_Male_50_to_64_years       0.35259054  0.09757265  3.6136 0.0003257 ***
White_Male_65_years_and_over    0.46833455  0.13366773  3.5037 0.0004908 ***
Black_Female_10_to_19_years    -0.46902450  0.43012668 -1.0904 0.2759333    
Black_Female_20_to_29_years    -0.85330286  0.19903094 -4.2873 2.088e-05 ***
Black_Female_30_to_39_years     0.67000107  0.29117489  2.3010 0.0217112 *  
Black_Female_40_to_49_years     0.20211428  0.24593810  0.8218 0.4114918    
Black_Female_50_to_64_years     0.27010596  0.24815423  1.0885 0.2768024    
Black_Female_65_years_and_over -0.12661080  0.36916618 -0.3430 0.7317382    
Black_Male_10_to_19_years       0.74235062  0.44292888  1.6760 0.0942266 .  
Black_Male_20_to_29_years       0.79460625  0.21556244  3.6862 0.0002470 ***
Black_Male_30_to_39_years      -0.29673477  0.30748553 -0.9650 0.3348916    
Black_Male_40_to_49_years      -0.14562558  0.30662465 -0.4749 0.6349984    
Black_Male_50_to_64_years      -0.14915192  0.27623210 -0.5400 0.5894186    
Black_Male_65_years_and_over   -0.96725742  0.58296284 -1.6592 0.0975643 .  
Other_Female_10_to_19_years    -2.24967607  0.57904598 -3.8851 0.0001129 ***
Other_Female_20_to_29_years    -0.27537653  0.30109748 -0.9146 0.3607592    
Other_Female_30_to_39_years    -2.45674942  0.40420766 -6.0779 2.093e-09 ***
Other_Female_40_to_49_years     1.44842440  0.52993251  2.7332 0.0064453 ** 
Other_Female_50_to_64_years     1.61920629  0.40211618  4.0267 6.336e-05 ***
Other_Female_65_years_and_over  1.40148342  0.29971128  4.6761 3.569e-06 ***
Other_Male_10_to_19_years       2.48313426  0.54927918  4.5207 7.345e-06 ***
Other_Male_20_to_29_years       0.25699446  0.29912664  0.8591 0.3905803    
Other_Male_30_to_39_years       2.49248478  0.43813341  5.6889 1.948e-08 ***
Other_Male_40_to_49_years      -1.53468291  0.51566283 -2.9761 0.0030294 ** 
Other_Male_50_to_64_years      -2.01173714  0.48027699 -4.1887 3.200e-05 ***
Other_Male_65_years_and_over   -2.05964386  0.42138889 -4.8878 1.291e-06 ***
Unemployment_rate              -0.00605069  0.00626903 -0.9652 0.3348243    
Poverty_rate                    0.00210437  0.00285683  0.7366 0.4616286    
Population_log                  0.40657802  0.14084185  2.8868 0.0040237 ** 
police_per_100k_lag             0.00021813  0.00016227  1.3443 0.1793315    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Total Sum of Squares:    12.935
Residual Sum of Squares: 6.4494
R-Squared:      0.5014
Adj. R-Squared: 0.42727
F-statistic: 15.6726 on 41 and 639 DF, p-value: < 2.22e-16

Multicollinearity analysis

How did the above happen?

The analysis dataframes are very similar yet rendered very different results.

- different number of columns: 20 vs 50
[1] TRUE

The only difference between the two dataframes rests in how the demographic variables were parameterized.

[1] "Black_Male_15_to_19_years" "Black_Male_20_to_39_years"
[3] "Other_Male_15_to_19_years" "Other_Male_20_to_39_years"
[5] "White_Male_15_to_19_years" "White_Male_20_to_39_years"
 [1] "Black_Female_10_to_19_years"    "Black_Female_20_to_29_years"   
 [3] "Black_Female_30_to_39_years"    "Black_Female_40_to_49_years"   
 [5] "Black_Female_50_to_64_years"    "Black_Female_65_years_and_over"
 [7] "Black_Male_10_to_19_years"      "Black_Male_20_to_29_years"     
 [9] "Black_Male_30_to_39_years"      "Black_Male_40_to_49_years"     
[11] "Black_Male_50_to_64_years"      "Black_Male_65_years_and_over"  
[13] "Other_Female_10_to_19_years"    "Other_Female_20_to_29_years"   
[15] "Other_Female_30_to_39_years"    "Other_Female_40_to_49_years"   
[17] "Other_Female_50_to_64_years"    "Other_Female_65_years_and_over"
[19] "Other_Male_10_to_19_years"      "Other_Male_20_to_29_years"     
[21] "Other_Male_30_to_39_years"      "Other_Male_40_to_49_years"     
[23] "Other_Male_50_to_64_years"      "Other_Male_65_years_and_over"  
[25] "White_Female_10_to_19_years"    "White_Female_20_to_29_years"   
[27] "White_Female_30_to_39_years"    "White_Female_40_to_49_years"   
[29] "White_Female_50_to_64_years"    "White_Female_65_years_and_over"
[31] "White_Male_10_to_19_years"      "White_Male_20_to_29_years"     
[33] "White_Male_30_to_39_years"      "White_Male_40_to_49_years"     
[35] "White_Male_50_to_64_years"      "White_Male_65_years_and_over"  

Clearly, this had an effect on the results of the analysis.

Let’s explore how this occured.

When seemingly independent variables are highly related to one another, the relationships estimated in an analysis may be distorted.

In regression analysis, this distortion is often a byproduct of a violation of the independence assumption. This distortion, if large enough, can impact statistical inference.

There are several ways we can diagnose multicollinearity.

Correlation

Again, multicollinearity often occurs when independent variables are highly related to one another. Consequently, we can evaluate these relationships be examining the correlation between variable pairs.

It is important to note that multicollinearity and correlation are not one and the same. Correlation can be thought of as the strength of the relationship between variables. On the other hand, multicollinearity can be thought of the the violation of the independence assumption that is a consequence of this correlation in a regression analysis.

Scatterplots

 [1] "STATE"                     "YEAR"                     
 [3] "Black_Male_15_to_19_years" "Black_Male_20_to_39_years"
 [5] "Other_Male_15_to_19_years" "Other_Male_20_to_39_years"
 [7] "White_Male_15_to_19_years" "White_Male_20_to_39_years"
 [9] "Unemployment_rate"         "Poverty_rate"             
[11] "Viol_crime_count"          "Population"               
[13] "police_per_100k_lag"       "RTC_LAW_YEAR"             
[15] "RTC_LAW"                   "TIME_0"                   
[17] "TIME_INF"                  "Viol_crime_rate_1k"       
[19] "Viol_crime_rate_1k_log"    "Population_log"           

Coefficient estimate instability

sims <- 250

# DONOHUE

# round(dim(DONOHUE_DF)[1]/2)
samps_DONOHUE <- lapply(rep(dim(DONOHUE_DF)[1]-1, sims),
       function(x)DONOHUE_DF[sample(nrow(DONOHUE_DF),
                                     size = x, replace = FALSE),])

fit_nls_on_bootstrap_DONOHUE <- function(split){
  plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
      data = data.frame(split),
      index = c("STATE","YEAR"),
      model = "within",
      effect = "twoways")
}
  
samps_models_DONOHUE <- lapply(samps_DONOHUE, fit_nls_on_bootstrap_DONOHUE)

samps_models_DONOHUE <- samps_models_DONOHUE %>%
  map(tidy)

names(samps_models_DONOHUE) <- paste0("DONOHUE_",1:length(samps_models_DONOHUE))

simulations_DONOHUE <- samps_models_DONOHUE %>%
  bind_rows(.id = "ID") %>%
  mutate(Analysis = "Analysis 1")

## LOTT

samps_LOTT <- lapply(rep(round(dim(LOTT_DF)[1]/2), sims),
       function(x) LOTT_DF[sample(nrow(LOTT_DF),
                                  size = x, replace = FALSE),])

fit_nls_on_bootstrap_LOTT <- function(split){
  plm(LOTT_fmla,
      data = data.frame(split),
      index = c("STATE","YEAR"),
      model = "within",
      effect = "twoways")
}
  
samps_models_LOTT <- lapply(samps_LOTT, fit_nls_on_bootstrap_LOTT)

samps_models_LOTT <- samps_models_LOTT %>%
  map(tidy)

names(samps_models_LOTT) <- paste0("LOTT_",1:length(samps_models_LOTT))

simulations_LOTT <- samps_models_LOTT %>%
  bind_rows(.id = "Analysis") %>%
  mutate(Analysis = "Analysis 2")

simulations <- bind_rows(simulations_DONOHUE,
                         simulations_LOTT)

simulation_plot <- simulations %>%
  filter(term=="RTC_LAWTRUE") %>%
  ggplot(aes(x = Analysis, y = estimate)) + 
  geom_jitter(alpha = 0.25,
              width = 0.1) + 
  labs(title = "Coefficient instability",
       subtitle = "Estimates sensitive to observation deletions",
       x = "Term",
       y = "Coefficient",
       caption = "Results from simulations") + 
  theme_minimal() +
  theme(axis.title.x = element_blank())

simulation_plot

VIF

              RTC_LAWTRUE White_Male_15_to_19_years White_Male_20_to_39_years 
                 1.157336                  1.753266                  2.608183 
Black_Male_15_to_19_years Black_Male_20_to_39_years Other_Male_15_to_19_years 
                 1.332589                  2.283867                  1.681014 
Other_Male_20_to_39_years         Unemployment_rate              Poverty_rate 
                 1.407883                  1.311134                  1.198027 
           Population_log       police_per_100k_lag 
                 1.176024                  1.117583 
                   RTC_LAWTRUE    White_Female_10_to_19_years 
                      1.792652                     175.910307 
   White_Female_20_to_29_years    White_Female_30_to_39_years 
                     38.695213                      79.741526 
   White_Female_40_to_49_years    White_Female_50_to_64_years 
                     29.102734                      41.365488 
White_Female_65_years_and_over      White_Male_10_to_19_years 
                     22.912619                     180.294231 
     White_Male_20_to_29_years      White_Male_30_to_39_years 
                     37.977938                      92.987226 
     White_Male_40_to_49_years      White_Male_50_to_64_years 
                     30.083073                      55.343121 
  White_Male_65_years_and_over    Black_Female_10_to_19_years 
                     27.351523                     140.154114 
   Black_Female_20_to_29_years    Black_Female_30_to_39_years 
                     56.959351                     171.914269 
   Black_Female_40_to_49_years    Black_Female_50_to_64_years 
                     46.707169                      71.514828 
Black_Female_65_years_and_over      Black_Male_10_to_19_years 
                    106.967399                     131.486665 
     Black_Male_20_to_29_years      Black_Male_30_to_39_years 
                     63.713636                     183.271248 
     Black_Male_40_to_49_years      Black_Male_50_to_64_years 
                     43.494574                      66.342952 
  Black_Male_65_years_and_over    Other_Female_10_to_19_years 
                     92.090941                     184.637460 
   Other_Female_20_to_29_years    Other_Female_30_to_39_years 
                     51.812396                      63.776558 
   Other_Female_40_to_49_years    Other_Female_50_to_64_years 
                    141.953648                     234.097628 
Other_Female_65_years_and_over      Other_Male_10_to_19_years 
                     60.354538                     212.258459 
     Other_Male_20_to_29_years      Other_Male_30_to_39_years 
                     59.985342                      65.956192 
     Other_Male_40_to_49_years      Other_Male_50_to_64_years 
                    108.867459                     384.831305 
  Other_Male_65_years_and_over              Unemployment_rate 
                     17.361666                       1.829250 
                  Poverty_rate                 Population_log 
                      1.343519                       3.838949 
           police_per_100k_lag 
                      1.491501 
[1] 2.608183
[1] 384.8313

\[\frac{1}{1-R_{i}^{2}}\]

Synthesis

Data Visualization


Summary


Suggested Homework


---
title: "Open Case Studies: Examination of Multicollinearity Influence on Inference Using Right-to-Carry Gun Law and Violent Crime Data"
author: "Michael Ontiveros, Carrie Wright, PhD."
css: style.css
output:
  html_document:
    self_contained: yes
    code_download: yes
    highlight: tango
    number_sections: no
    theme: cosmo
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes
  word_document:
    toc: yes
---

<style>
#TOC {
  background: url("https://opencasestudies.github.io/img/logo.jpg");
  background-size: contain;
  padding-top: 240px !important;
  background-repeat: no-repeat;
}
</style>


---


```{r setup, include=FALSE}
knitr::opts_chunk$set(include = TRUE, comment = NA, echo = TRUE,
                      message = FALSE, warning = FALSE, cache = FALSE, fig.width=10, fig.height=7,
                      fig.align = "center", out.width = '90%')
library(here)
library(knitr)
```

#### {.outline }
```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "mainplot.png"))
```

####

## {.disclaimer_block}

**Disclaimer**: The purpose of the [Open Case Studies](https://opencasestudies.github.io){target="_blank"} project is **to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data**. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts. 

## {.liscence_block}

This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 [(CC BY-NC 3.0)](https://creativecommons.org/licenses/by-nc/3.0/us/){target="_blank"}  United States License.

# **Motivation**
*** 

This case study will introduce the topic of multicolinearity. We will do so by showcasing a real world example where multicolinearity in part resulted in historically contriversial and conflicting findings about the influence of the adoption of right-to-carry (RTC) concealed handgun laws on violent crime rates in the United States. 

We will focus on two articles:

1) The first analysis by [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} published in 1996 suggests that RTC laws reduce violent crime. Lott authored a book extending these findings in 1998 called [***More Guns, Less Crime***](https://en.wikipedia.org/wiki/More_Guns,_Less_Crime){target="_blank"}.

```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "Lott.png"))
```

2) The second analysis is a recent article by [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} published in 2017 that suggests that RTC laws increase violent crime. Donohue has also published previous articles with titles such as [***"Shooting down the "More Guns, Less Crime" Hypothesis***](https://www.jstor.org/stable/1229603?seq=1){target="_blank"} 

```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "Donohue.png"))
```

This has been a controversial topic as many other articles also had conflicting results. See [here](https://en.wikipedia.org/wiki/More_Guns,_Less_Crime){target="_blank"} for a list of studies.

The [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} article discusses how there are many other important methodolical aspects besides multicolinearity that could account for the historically conflicting results in these previous papers.

In fact, nearly every aspect of the data analysis process was different between the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} analysis and the [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} analysis.

```{r, echo=FALSE, out.height = '75%', out.width = '75%', fig.align='center'}
knitr::include_graphics(here("img", "Educational_Graphic1.jpg"))
```

However, we will focus particularly on multicolinearity and we will explore how it can influence linear regression analyses and result in different conclusions. 

This analysis will demonstrate how methodological details can be critically influential for our overall conclusions and can result in important policy related consequences. This [article]((https://www.nber.org/papers/w23510.pdf){target="_blank"}) will provide a basis for the motivation. 

#### {.reference_block}

John J. Donohue et al., Right‐to‐Carry Laws and Violent Crime: A Comprehensive Assessment Using Panel Data and a State‐Level Synthetic Control Analysis. *Journal of Empirical Legal Studies*, 16,2 (2019).

David B. Mustard & John Lott. Crime, Deterrence, and Right-to-Carry Concealed Handguns. *Coase-Sandor Institute for Law & Economics* Working Paper No. 41, (1996).

####


Here you can see the differences in the data used in the featured RTC articles:


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img",'Donohue_Table2.png'))
```


We will perform analyses similar to those in these articles, however **we will not try to recreate them**, instead we will simplify our analysis to allow us to focus on multicolinearity.


Therefore we will use a subset of the listed explanatory variables and they will be consistent for both analyses that we will perform, with the exception that one analysis will have 6 demographic variables like the analysis in the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} article and the other will have 36 demogrpahic variables like the analysis in the [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} article.


# **Main Question**
*** 

#### {.main_question_block}
<b><u> Our main question: </u></b>

1) How does the inclusion of different numbers of age groups influence the results of an analysis of right to carry laws and violence rates?

####


# **Learning Objectives** 
*** 

<u>**Statistical Learning Objectives:**</u> 

In this case study, students will learn:  
1) what multicolinearity is and how it can influence linear regression coefficients  
2) how to look for the presence of multicolinarity  
3) the difference between multicolinearity and correlation  

<u>**Data science Learning Objectives:**</u>

1) joining data from multiple sources (dplyr)  
2) reshaping data into different formats (tidyr)  
2) visualizations (ggplot2)  


We will especially focus on using packages and functions from the [`Tidyverse`](https://www.tidyverse.org/){target="_blank"}, such as `dplyr` and `ggplot2`. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially efficient.


```{r, out.width = "20%", echo = FALSE, fig.align ="center"}
include_graphics("https://tidyverse.tidyverse.org/logo.png")
```

# **Context**
***

So what exactly is a **right-to-carry law**?

It is a law thatspecifies if and how citizens are allowed to have a firearm on their person or nearby (for example in the citizen's car) in public. 

The [Second Amendment](https://en.wikipedia.org/wiki/Second_Amendment_to_the_United_States_Constitution){target="_blank"} to the United States Constitution guarantees the right to "keep and bear arms". The amendment was ratified in 1791 as part of the [Bill of Rights](https://en.wikipedia.org/wiki/United_States_Bill_of_Rights){target="_blank"}.

```{r, echo=FALSE, out.height = '50%', out.width = '50%', fig.align='center'}
knitr::include_graphics("https://upload.wikimedia.org/wikipedia/commons/7/79/Bill_of_Rights_Pg1of1_AC.jpg")
```

However, there are no federal laws about carrying firearms in public. 

These laws are created and enforced at the state level. Sates vary greatly in their laws about the right to carry firearms. Some require extensive effort to obtain a permit to legally carry a firearm, while other states require very minimal effort to legally carry a firearm.


According to Wikipedia about the history of right-to-carry policies in the United States:

> Public perception on concealed carry vs open carry has largely flipped. In the early days of the United States, open carrying of firearms, long guns and revolvers was a common and well-accepted practice. Seeing guns carried openly was not considered to be any cause for alarm. Therefore, anyone who would carry a firearm but attempt to conceal it was considered to have something to hide, and presumed to be a criminal. For this reason, concealed carry was denounced as a detestable practice in the early days of the United States.

> Concealed weapons bans were passed in Kentucky and Louisiana in 1813. (In those days open carry of weapons for self-defense was considered acceptable; concealed carry was denounced as the practice of criminals.) By 1859, Indiana, Tennessee, Virginia, Alabama, and Ohio had followed suit. By the end of the nineteenth century, similar laws were passed in places such as Texas, Florida, and Oklahoma, which protected some gun rights in their state constitutions. Before the mid 1900s, most U.S. states had passed concealed carry laws rather than banning weapons completely. Until the late 1990s, many Southern states were either "No-Issue" or "Restrictive May-Issue". Since then, these states have largely enacted "Shall-Issue" licensing laws, with numerous states legalizing "Unrestricted concealed carry".

See [here](https://en.wikipedia.org/wiki/History_of_concealed_carry_in_the_U.S.){target="_blank"} for more information.

Here are the general categories of Right to Carry Laws:

```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "RTC.png"))
```
[source](https://www.nraila.org/gun-laws/){target="_blank"}


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "RTC_map.png"))
```

[source](https://www.nraila.org/gun-laws/){target="_blank"}

You can see that none of the fifty states have no-issue laws currently, meaning that all states allow the right to carry firearms at least in some way, however the level of restrictions is dramatically different from one state to another.

Here you can see how these laws have changed over time around the country:
```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics("https://upload.wikimedia.org/wikipedia/commons/thumb/5/5a/Right_to_Carry%2C_timeline.gif/620px-Right_to_Carry%2C_timeline.gif")
```

There is variation from state to state even within the same general category:

For example here are the [current carry laws in Idaho](https://www.nraila.org/gun-laws/state-gun-laws/idaho/) which is considered an "Unrestricted - no permit required" state:

>Idaho permits the open carrying of firearms.

>Idaho law permits both residents and non-residents who are at least 18 years old to carry concealed weapons, without a carry license, outside the limits of or confines of any city, provided the person is not otherwise disqualified from being issued a license to carry.

>A person may also carry concealed weapons on or about his or her person, without a license, in the person’s own place of abode or fixed place of business, on property in which the person has any ownership or leasehold interest, or on private property where the person has permission to carry from any person who has an ownership or leasehold interest in that property. 

>State law also allows any resident of Idaho or a current member of the armed forces of the United States to carry a concealed handgun without a license to carry, provided the person is over 18 years old and not disqualified from being issued a license to carry concealed weapons under state law. An amendment to state law that takes effect on July 1, 2020 changes the reference in the above law from “a resident of Idaho” to “any citizen of the United States.”  


And here are the [current carry laws in Arizona](https://www.nraila.org/gun-laws/state-gun-laws/arizona/) which is also considered an "Unrestricted- - no permit required" state:

> Arizona respects the right of law abiding citizens to openly carry a handgun.

> Any person 21 years of age or older, who is not prohibited possessor, may carry a weapon openly or concealed without the need for a license. Any person carrying without a license must acknowledge and comply with the demands of a law enforcement officer when asked if he/she is carrying a concealed deadly weapon, if the officer has initiated an "investigation" such as a traffic stop.

Notice that citizens in Idaho only need to be 18 to carry a firearm, whereas they must be 21 in Arizona. 


In contrast here is an example of [current carry laws in Maryland](https://www.nraila.org/gun-laws/state-gun-laws/maryland/) which is considered a "Rights Restricted-Very Limited Issue" state:

>Carrying and Transportation in Vehicles
It is unlawful for any person without a permit to wear or carry a handgun, openly or concealed, upon or about his person.  It is also unlawful for any person to knowingly transport a handgun in any vehicle traveling on public roads, highways, waterways or airways, or upon roads or parking lots generally used by the public. This does not apply to any person wearing, carrying or transporting a handgun within the confines of real estate owned or leased by him, or on which he resides, or within the confines of a business establishment owned or leased by him.

>Permit To Carry
Application for a permit to carry a handgun is made to the Secretary of State Police.  In addition to the printed application form, the applicant should submit a notarized letter stating the reasons why he is applying for a permit.


avocado....Right to carry and covid masks?

# **Limitations**
*** 
There are some important considerations regarding this data analysis to keep in mind: 

1) We do not use all of the data used by either the  [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} or [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} analyses, nor do we perform the same analysis of each article. We instead perform a much simpler analysis with less variables for the purposes of illustration of the concept of multicollinearity and its influence on regression coefficients, not to reproduce either analysis.

2) Because our analysis is an oversimplification, our analysis should not be used for determining policy changes, instead we suggest that users consult with a specialist.


We would also like to note that...AVOCADO
It is important that we do not treat race as an objective measure. Despite this, it can be used to advance scientific inquiry. For more information on this topic, we have included a link to a [paper on the use of race as a measure in epidemiology](https://academic.oup.com/epirev/article/22/2/187/456942). 


We will begin by loading the packages that we will need:

```{r}
library(here)
library(readxl)
library(readr)
library(pdftools)
library(dplyr)
library(magrittr)
library(tidyr)
library(stringr)

library(car) # vif function
library(plm) # fixed effect model, linear regression
library(broom) # tidy output
library(tidyverse) # general wrangling functions
library(cowplot) # to produce plot of plots 
library(GGally)
library(ggrepel)
library(scales)
library(latex2exp)
library(viridis)
library(ggcorrplot)
library(rsample)

set.seed(999)
```


 Package   | Use                                                                         
---------- |-------------
[here](https://github.com/jennybc/here_here){target="_blank"}       | to easily load and save data
[readr](https://readr.tidyverse.org/){target="_blank"}      | to import the CSV file data
[car]  | to calculate vif values

The first time we use a function, we will use the `::` to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.


# **What are the data?**
***

Below is a table from the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf) paper that shows the data used in both analyses, where DAW stands for [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} and LM stands for [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"}.


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "Donohue_AppendixJ.png"))
```

We will be using a subset of these variables, which are highlighted in green:


```{r, echo=FALSE, out.height = '100%', out.width = '100%', fig.align='center'}
knitr::include_graphics(here("img", "ourdata.png"))
```


# **Data Import**
***



## Demographic and Population data

To obtain information about age, sex, and race, and overall population we will use US Census Bureau data, just like both of the articles. The cesnus data is available for different time spans. Here are the links for the years used in our analysis. We will use data from 1977 to 2010.

Data   | Link                                                                        
---------- |-------------
**years 1977 to 1979**  | [link](https://www2.census.gov/programs-surveys/popest/tables/1900-1980/state/asrh/)  
**years 1980 to 1989**  | [link](https://www2.census.gov/programs-surveys/popest/tables/1980-1990/counties/asrh/) * county data was used for this decade
**years 1990 to 1999**  | [link](https://www2.census.gov/programs-surveys/popest/tables/1990-2000/state/asrh/)
**years 2000 to 2010**  | [link](https://www.census.gov/data/datasets/time-series/demo/popest/intercensal-2000-2010-state.html) <br> [technical documentation](https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2000-2010/intercensal/state/st-est00int-alldata.pdf)

To import the data we will use the `read_csv()` function of the `readr` package for the csv files. In some decades, there are separate files for each year, we will read each of these together using the base `list.files()` function to get all of the names for each file and then the `map()` function of the `purrr` package to apply the `read_csv()` function on all of the file paths in the list created by `list.files()`. For years that are txt files we will use `read_table2()` also fo the `readr` package. The `read_table2()` function, unlike the `read_table()`,  allows for any number of whitespace characters between columns, and the lines can be of different lengths.

AVOCADO I am a bit confused about the last decade... it's only one file but it seems to need map...

```{r}

dem_77_79 <- read_csv("docs/Demographics/Decade_1970/pe-19.csv", skip = 5)

dem_80_89 <- list.files(recursive = TRUE,
                  path = "docs/Demographics/Decade_1980/",
                  pattern = "*.csv",
                  full.names = TRUE) %>% 
  map(~read_csv(., skip=5))

dem_90_99 <- list.files(recursive = TRUE,
                  path = "docs/Demographics/Decade_1990/",
                  pattern = "*.txt",
                  full.names = TRUE) %>% 
  map(~read_table2(., skip = 14))


dem_90_99 <- dem_90_99 %>%
  map_df(bind_rows)


dem_00_10_2 <- read_csv("docs/Demographics/Decade_2000/st-est00int-alldata.csv")

dem_00_10 <- list.files(recursive = TRUE,
                  path = "docs/Demographics/Decade_2000/",
                  pattern = "*.csv",
                   full.names = TRUE) %>% 
   map(~read_csv(.))

head(dem_00_10)

```

Notice that the `STATE` variable for the demographic data is numeric. That is because it is encoded by [Federal Information Processing Standard (FIPS) state codes](https://en.wikipedia.org/wiki/Federal_Information_Processing_Standard_state_code){target="_blank". Thus we also need to import data  about FIPS encoding so that we can identify what data corresponds to what state.


##State FIPS codes

The following data was downloaded from the [US Census Bureau](https://www.census.gov/geographies/reference-files/2014/demo/popest/2014-geocodes-state.html){target="_blank"}.

To import the data we will use the `read_xls()` function of the `readxl` package. Since the first five lines of this excel is information about the source of the data and when it was released, we need to skip importing these lines using the `skip` argument so that the data has the same number of columns for each row. 

```{r}
knitr::include_graphics(here("img", "FIPS.png"))

```

```{r}
STATE_FIPS <- read_xls("docs/State_FIPS_codes/state-geocodes-v2014.xls", skip = 5)
(STATE_FIPS)
```

## Police staffing data
The following data was downloaded from the [Federal Bureau of Investigation](https://crime-data-explorer.fr.cloud.gov/downloads-and-docs). 


The `read_csv()` function of the `readr` package guesses what the class is for each variable, but sometimes it makes mistakes. It is good to specify the class for variables if you know them. We know that we want the variables about male and female counts to be numeric. We can specify that using the `col_types =` argument. See [here](https://readr.tidyverse.org/articles/readr.html) and [here](https://cran.r-project.org/web/packages/readr/vignettes/readr.html) for more information.

```{r}
ps_data <- read_csv("docs/Police_staffing/pe_1960_2018.csv")
ps_data <- read_csv("docs/Police_staffing/pe_1960_2018.csv",
                    col_types = cols(male_total_ct = "n",
                                     female_total_ct = "n"))

ps_data <- read_csv("docs/Police_staffing/pe_1960_2018.csv",
                   col_types =  cols(male_total_ct = col_double(),
                                   female_total_ct = col_double()))
head(ps_data)                            
```



## Unemplyment data

The following data was downloaded from the [U.S. Bureau of Labor Statistics](https://data.bls.gov/cgi-bin/dsrv?la). 

There are excel files for each state.  As you can see, there are many rows to skip to make sure that there are the same number of columns for each row. We can also see that the state name is located in a couple of the first rows. 

```{r}
knitr::include_graphics(here("img", "Unemp.png"))
```

We can also see that here if we just try to read in the files directly.

```{r}

ue_rate_data <- list.files(recursive = TRUE,
                  path = "docs/Unemployment",
                  pattern = "*.xlsx",
                  full.names = TRUE) %>% 
  map(~read_xlsx(.))
      
head(ue_rate_data)[1]
```

So now we will skip the first 10 lines. And also create a names tibble that contains only the cell with the state information.

```{r}
 
 ue_rate_data <- list.files(recursive = TRUE,
                  path = "docs/Unemployment",
                  pattern = "*.xlsx",
                  full.names = TRUE) %>% 
  map(~read_xlsx(., skip = 10))
  
head(ue_rate_data[1])
```

To get the state name for each file using the `map()` function to perform functions across all of the files, we will specifically import only a small range of cells using the `range = ` argument and then grab the cell that has state information based on it's location within the range of cells imported using `c()` and then use the base `unlist()` function to unlist the list that this creates.

```{r}
ue_rate_names <- list.files(recursive = TRUE,
                  path = "docs/Unemployment",
                  pattern = "*.xlsx",
                  full.names = TRUE) %>%
  map(~read_xlsx(., range = "B4:B6")) %>%
  map(., c(1,2)) %>%
  unlist()

ue_rate_names
```
## Poverty data
Extracted from Table 21 from [US Census Bureau Poverty Data ](https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-people.html)

AVOCado strange issue

```{r}

#**persistent warning from unknown origin** https://community.rstudio.com/t/persistent-unknown-or-uninitialised-column-warnings/64879

#solution to above is alledgedly: "In any case the suggested approach is to initialize the column"


poverty_rate_data <- read_xls("docs/Poverty/hstpov21.xls", skip=2) #This may cause initialization issue, not easily reproducible (even after restarting R)

head(poverty_rate_data)
```

We can see that this will require some wranlging to make the data more usable. 

## Violent crime

Violent crime data was obtained from [here](https://www.ucrdatatool.gov/Search/Crime/State/StatebyState.cfm) This data is a bit trickier because of spaces and `/` in the column names, thus the `read_lines()` function of the `readr` package works better than the `read_csv()` function.


```{r}
knitr::include_graphics(here("img", "crime.png"))
```

```{r}
crime_data <- read_lines("docs/Crime/CrimeStatebyState.csv", skip = 2, skip_empty_rows = TRUE)
head(crime_data)

```

We can see that this data will also require some wranlging to make it more usable. 

## Right-to-carry data

This data is extracted from table in [Donohue paper](https://www.nber.org/papers/w23510.pdf). We will use the function `pdf_text()`  of the `pdftools` package to import the pdf document.

```{r}

if(!file.exists(here("docs", "w23510.pdf"))){
  url <- "https://www.nber.org/papers/w23510.pdf"
  utils::download.file(url, here("docs", "w23510.pdf"))
}

DAWpaper <- pdf_text(here("docs", "w23510.pdf"))

head(DAWpaper[1])

```

Again, this data will also require quite a bit of wrangling.



# **Data Wrangling**
***
## State FIPS codes

Let's take a look at our state FIPS data to see if it needs any cleaning or reshaping.

```{r}
head(STATE_FIPS)
```

We only need the last two columns, but we might want to rename them. The `Name` variable is vague. The variable with the FIPS code is called `State\n(FIPS)`. To get rid of the new line in this variable name and to change the `Name` variable to something more informative, we will use the `rename()` function of the `dplyr` package.  To use this function, we need to list the new name first followed by `=` and then the existing variable. We can rename multiple variables at the same time by using a comma to separate the variables we are renaming. We will use the `select()` function also of the `dplyr` package just to keep these variables, and we will filter out the rows with FIPS values of `00` with the `filter()` function, agian also part of the `dplyr` package. we will specify that we want `STATEFP` values that are not equal to `00` by using this operator: `!=`. We will also use the double pipe operator `%<>%` of the `magrittr` package which allows us to use data as iuput and then reassign it after we peform sum functions using it.

```{r}

STATE_FIPS %<>% 
dplyr::rename( STATEFP = `State\n(FIPS)`,
                 STATE = Name) %>%
    dplyr::select(STATEFP, STATE) %>%
    dplyr::filter(STATEFP != "00")

STATE_FIPS

```

## Demographics

#### 1977-1979

Now let's take a look at our demographic data across the decades that we wish to study. If you have very wide data (meaning it has many columns), one way to view the data so that you can see all of the columns at the same time is to use the `glimpse()` function of the `dplyr` package. 

Taking a look at the first decade of data, we can see that the `Race/Sex Indicator` contains two types of data, the race and the sex. This does not follow the tidy data philosophy, where each cell of a tibble should only contain one piece of information. Typically one might think of using the `separate()` function of the `tidyr` package to split this variable into two. However, one of the race values is `Other races` and since this also has a space, this makes separating this data more tricky.

Instead we will use the `str_extract()` function of the `stringr` package and the `mutate()` function of the `dplyr` package. The "mutate()" will allow us to create new variables, and "str_extract()" function  will allow us to match specific patterns and pull out matches to those patterns. Therefore, if the `Race/Sex Indicator` value is `Other races male` and if we extract patterns matching either `"male"` or `"female"` which we can specify like this `pattern = "male|female"` then, the value will be `male`.

First we need to rename the `Race/Sex Indicator` varaible to not have spaces so that it is compatible with the `str_extract()` function.

We also want to rename a couple of variables to be simpler and filter the data to only include the years of the data we are interested in, as well as remove some variables that we dont need like the `FIPS State Code`. We can remove variables by using the `select()` function with a `-` minus sign in front of the variable we wish to remove.

```{r}
dplyr::glimpse(dem_77_79)


dem_77_79 <- dem_77_79 %>%
  rename("race_sex" =`Race/Sex Indicator`) %>%
  mutate(SEX = str_extract(race_sex, "male|female"),
        RACE = str_extract(race_sex, "Black|White|Other"))%>%
  select(-`FIPS State Code`, -`race_sex`) %>%
  rename("YEAR" = `Year of Estimate`,
        "STATE" = `State Name`) %>%
  filter(YEAR %in% 1977:1979)

glimpse(dem_77_79)
```

That's looking pretty  good! We also want to take all the age group variabels and make one variable that is the age group name and one that is the value of the population count for that age group. To do this we will use the `pivot_longer()` function of the `tidyr` package. To use this function, we need to use the `cols` argument to indicate which columns we want to pivot. We also name the new variables we will create with the `names_to` and `values_to` arguments. The `names_to` will be the name of the variable that will identify each age group and `values_to` will be the name of the variable that contains the corresponding population values.
```{r}
dem_77_79 <- dem_77_79 %>%
  pivot_longer(cols=contains("years"),
               names_to = "AGE_GROUP",
               values_to = "SUB_POP")

glimpse(dem_77_79)
```

We also want to get data about the total population for the state for each year.

To do so we can sum all the values for the `SUB_POP` variable that we just created. To do this we can use the `group_by` and `summarize()` functions of the `dplyr` package. The `group_by()` function specifies how we want to calculate  our sum, that we would like to calculate it for each year and each state individually. Thus, all the values that have the same `STATE` and `YEAR` values will be summed together, rather than summing using all of the values in the `SUB_POP` variable. The `.groups` argument allows us to remove the grouping after we peform the calculation with `summarize()`.

```{r}
pop_77_79 <- dem_77_79 %>%
  group_by(YEAR, STATE) %>%
  summarize("TOT_POP" = sum(SUB_POP), .groups = "drop") 

pop_77_79 
```


 Now we will add the population value to the demographic tibble using the `left_join()` function of the `dplyr` package. It is imporant that we specify how this should be done, that the `YEAR` and `STATE` variable vlaues should match eachother. This will place the `dem_77_79` variables to the left of the `pop_77_79` data. 
 
```{r}
dem_77_79 <- dem_77_79 %>%
  left_join(pop_77_79, by = c("YEAR","STATE"))

dem_77_79
```

We will also calculate the percentage that each group makes up of the total population, by dividing the `SUB_POP` by the `TOT_POP` and multiplying by 100 using the `mutate()` function. we will also remove the other population variables.

```{r}
dem_77_79 <- dem_77_79 %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  select(-SUB_POP, -TOT_POP)

dem_77_79
```


#### 1980-1989

For this decade each year is a separate tibble and they are combined as a list.
```{r}
class(dem_80_89)
```

So the first thing we need to do is combine each tibble of the list together. We can do that using the `bind_rows()` function of `dplyr` which appends the data together based on the presence of columns with the same name in the different tibbles. We will use the `map_df()` function of the `purrr` package to allow us to do this across each tibble in our list. 
```{r}
dem_80_89 <- dem_80_89 %>%
  map_df(bind_rows)

glimpse(dem_80_89 )
```

Great! Now our data is all together.

Now we will wrangle the data similarly to the previous decade.
```{r}
dem_80_89 <- dem_80_89 %>%
  rename("race_sex" =`Race/Sex Indicator`) %>%
  mutate(SEX = str_extract(race_sex, "male|female"),
        RACE = str_extract(race_sex, "Black|White|Other"))%>%
  select( -`race_sex`) %>%
  rename("YEAR" = `Year of Estimate`)
         
glimpse(dem_80_89)
```
Notice that this time the state information is based on the numeric FIPS value. We want only the first two values, as the rest indicate the county. We can use the `str_sub()` function of the `stringr` package for this. We will specify that we want to start at the first position and end at the second.  Just like `str_extract()` we need to rename this variable first so that it is compatible. 
```{r}
dem_80_89 %<>%
rename("STATEFP_temp" = "FIPS State and County Codes") %>%
mutate(STATEFP = str_sub(STATEFP_temp, start = 1, end = 2)) %>%
    left_join(STATE_FIPS, by = "STATEFP") %>%
  dplyr::select(-STATEFP)

glimpse(dem_80_89)
```

AVOCADO - why do we group by age_group sex and race this time but not with the last decade?

```{r}
dem_80_89 %<>%
  pivot_longer(cols=contains("years"),
               names_to = "AGE_GROUP",
               values_to = "SUB_POP_temp") %>%
  group_by(YEAR, STATE, AGE_GROUP, SEX, RACE) %>%
  summarise(SUB_POP = sum(SUB_POP_temp), .groups="drop")

dem_80_89
```
  
```{r}
pop_80_89 <- dem_80_89 %>%
  group_by(YEAR, STATE) %>%
  summarise("TOT_POP" = sum(SUB_POP), .groups = "drop") 


dem_80_89 <- dem_80_89 %>%
  left_join(pop_80_89, by = c("YEAR","STATE")) %>%
  mutate(PERC_SUB_POP = (SUB_POP/TOT_POP)*100) %>%
  dplyr::select(-SUB_POP, -TOT_POP)

dem_80_89
```

#### 1990-1999


```{r}
glimpse(dem_90_99)

colnames(dem_90_99) <- c("YEAR",
                         "STATEFP",
                         "Age",
                         "NH_W_M",
                         "NH_W_F",
                         "NH_B_M",
                         "NH_B_F",
                         "NH_AIAN_M",
                         "NH_AIAN_F",
                         "NH_API_M",
                         "NH_API_F",
                         "H_W_M",
                         "H_W_F",
                         "H_B_M",
                         "H_B_F",
                         "H_AIAN_M",
                         "H_AIAN_F",
                         "H_API_M",
                         "H_API_F")

dim(dem_90_99)

dem_90_99 <- dem_90_99 %>%
    mutate(W_M = NH_W_M + H_W_M,
           W_F = NH_W_F + H_W_F,
           B_M = NH_B_M + H_B_M,
           B_F = NH_B_F + H_B_F,
           AIAN_M = NH_AIAN_M + H_AIAN_M,
           AIAN_F = NH_AIAN_F + H_AIAN_F,
           API_M = NH_API_M + H_API_M,
           API_F = NH_API_F + H_API_F,
           n_na = rowSums(is.na(.))) %>%
  dplyr::select(-starts_with("NH_"), -starts_with("H_"))

dem_90_99 %>%
  group_by(n_na) %>%
  tally()

empty_rows_na <- dem_90_99 %>%
  group_by(n_na) %>%
  tally() %>%
  filter(n_na != 0) %>%
  pull(n_na)

dem_90_99 <- dem_90_99 %>%
  filter(n_na != empty_rows_na) %>%
  dplyr::select(-n_na)

sapply(dem_90_99, class)

summary(as.factor(dem_80_89$AGE_GROUP))

dem_90_99 <- dem_90_99 %>%
  mutate(AGE_GROUP = cut(Age,
                         breaks = seq(0,90, by=5),
                         right = FALSE,
                         labels = c("Under 5 years",
                                    "5 to 9 years",
                                    "10 to 14 years",
                                    "15 to 19 years",
                                    "20 to 24 years",
                                    "25 to 29 years",
                                    "30 to 34 years",
                                    "35 to 39 years",
                                    "40 to 44 years",
                                    "45 to 49 years",
                                    "50 to 54 years",
                                    "55 to 59 years",
                                    "60 to 64 years",
                                    "65 to 69 years",
                                    "70 to 74 years",
                                    "75 to 79 years",
                                    "80 to 84 years",
                                    "85 years and over")
                         )) %>%
  dplyr::select(-Age) %>%
  mutate(AGE_GROUP = as.character(AGE_GROUP))

sapply(dem_90_99, class)

dem_90_99 <- dem_90_99 %>%
  group_by(YEAR, STATEFP, AGE_GROUP) %>%
  summarise_at(vars(starts_with("W_"),
                    starts_with("B_"),
                    starts_with("AIAN_"),
                    starts_with("API_")), sum) %>%
  ungroup() %>%
  pivot_longer(cols = c(starts_with("W_"),
                    starts_with("B_"),
                    starts_with("AIAN_"),
                    starts_with("API_")),
               names_to = "RACE",
               values_to = "SUB_POP")

dem_90_99 <- dem_90_99 %>%
  mutate(SEX = case_when(str_detect(RACE, "_M") ~ "Male",
                         TRUE ~ "Female"),
         RACE = case_when(str_detect(RACE, "W_") ~ "White",
                          str_detect(RACE, "B_") ~ "Black",
                          TRUE ~ "Other")) %>%
  left_join(STATE_FIPS, by = "STATEFP") %>%
  dplyr::select(-STATEFP)

pop_90_99 <- dem_90_99 %>%
  group_by(YEAR, STATE) %>%
  summarise(TOT_POP = sum(SUB_POP), .groups = "drop")

dem_90_99 <- dem_90_99 %>%
  left_join(pop_90_99, by=c("YEAR", "STATE")) %>%
  mutate(PERC_SUB_POP = SUB_POP*100/TOT_POP) %>%
  dplyr::select(-SUB_POP, -TOT_POP)
```

#### 2000-2010

```{r}
dem_00_10 <- dem_00_10 %>%
  map_df(bind_rows)

glimpse(dem_00_10)

dem_00_10 <- dem_00_10 %>%
  dplyr::select(-ESTIMATESBASE2000,-CENSUS2010POP) %>%
  filter(REGION != 0,
         DIVISION != 0,
         SEX != 0,
         ORIGIN == 0,
         RACE != 0,
         AGEGRP != 0,
         STATE != 0) %>%
  dplyr::select(-REGION, -DIVISION, -ORIGIN, -STATE) %>%
  rename("STATE" = NAME,
         "AGE_GROUP" = AGEGRP) %>%
  mutate(SEX = factor(SEX,
                            levels = 1:2,
                            labels = c("Male",
                                    "Female")),
         RACE = factor(RACE,
                            levels = 1:6,
                            labels = c("White",
                                    "Black",
                                    rep("Other",4))),
         AGE_GROUP = factor(AGE_GROUP,
                            levels = 1:18,
                            labels = c("Under 5 years",
                                    "5 to 9 years",
                                    "10 to 14 years",
                                    "15 to 19 years",
                                    "20 to 24 years",
                                    "25 to 29 years",
                                    "30 to 34 years",
                                    "35 to 39 years",
                                    "40 to 44 years",
                                    "45 to 49 years",
                                    "50 to 54 years",
                                    "55 to 59 years",
                                    "60 to 64 years",
                                    "65 to 69 years",
                                    "70 to 74 years",
                                    "75 to 79 years",
                                    "80 to 84 years",
                                    "85 years and over"))) %>%
  mutate(SEX = as.character(SEX),
         RACE = as.character(RACE),
         AGE_GROUP = as.character(AGE_GROUP))
  
colnames(dem_00_10)

dem_00_10 <- dem_00_10 %>%
  pivot_longer(cols=contains("ESTIMATE"),
               names_to = "YEAR",
               values_to = "SUB_POP")

dem_00_10 <- dem_00_10 %>%
  mutate(YEAR = str_sub(YEAR, start=-4)) %>%
  mutate(YEAR = as.numeric(YEAR))

sapply(dem_00_10, class)

pop_00_10 <- dem_00_10 %>%
  group_by(YEAR, STATE) %>%
  summarise(TOT_POP = sum(SUB_POP), .groups = "drop")

dem_00_10 %>%
  left_join(pop_00_10, by=c("YEAR", "STATE")) %>%
  group_by(YEAR, STATE) %>%
  mutate(PERC_SUB_POP = SUB_POP*100/TOT_POP) %>%
  summarise(perc_tot = sum(PERC_SUB_POP), .groups = "drop") %>%
  mutate(poss_error = case_when(abs(perc_tot - 100) > 0 ~ TRUE, 
                                TRUE ~ FALSE)) %>%
  group_by(poss_error) %>%
  tally()

dem_00_10 <- dem_00_10 %>%
  left_join(pop_00_10, by=c("YEAR", "STATE")) %>%
  mutate(PERC_SUB_POP = SUB_POP*100/TOT_POP) %>%
  dplyr::select(-SUB_POP, -TOT_POP)
```




### Combining demographic data

```{r}
setequal(colnames(dem_77_79),colnames(dem_80_89))
setequal(colnames(dem_80_89),colnames(dem_90_99))
setequal(colnames(dem_90_99),colnames(dem_00_10))

head(dem_77_79)
head(dem_80_89)
head(dem_90_99)
head(dem_00_10)

length(summary(as.factor(dem_77_79$AGE_GROUP)))
length(summary(as.factor(dem_80_89$AGE_GROUP)))
length(summary(as.factor(dem_90_99$AGE_GROUP)))
length(summary(as.factor(dem_00_10$AGE_GROUP)))

dem <- bind_rows(dem_77_79,
                 dem_80_89,
                 dem_90_99,
                 dem_00_10)
  
dem %>%
  filter(RACE == "Other") %>%
  group_by(YEAR) %>%
  tally() %>%
  summarise(years_data = n())

2010 - 1977 + 1
  
DONOHUE_AGE_GROUPS <- c("15 to 19 years",
                        "20 to 24 years",
                        "25 to 29 years",
                        "30 to 34 years",
                        "35 to 39 years")

DONOHUE_RACE <- c("White",
                  "Black",
                  "Other")

DONOHUE_SEX <- c("Male")

dem_DONOHUE <- dem %>%
  filter(AGE_GROUP %in% DONOHUE_AGE_GROUPS,
         RACE %in% DONOHUE_RACE,
         SEX %in% DONOHUE_SEX) %>%
  mutate(AGE_GROUP = fct_collapse(AGE_GROUP, "20 to 39 years"=c("20 to 24 years",
                                                                "25 to 29 years",
                                                                "30 to 34 years",
                                                                "35 to 39 years"))) %>%
  mutate(AGE_GROUP = str_replace_all(AGE_GROUP," ","_")) %>%
  group_by(YEAR, STATE, RACE, SEX, AGE_GROUP) %>%
  summarise(PERC_SUB_POP = sum(PERC_SUB_POP), .groups = "drop") %>%
  unite(col = "VARIABLE", RACE, SEX, AGE_GROUP, sep = "_") %>%
  rename("VALUE"=PERC_SUB_POP)

LOTT_AGE_GROUPS_NULL <- c("Under 5 years",
                          "5 to 9 years")

LOTT_RACE <- c("White",
               "Black",
               "Other")

LOTT_SEX <- c("Male",
              "Female")

dem_LOTT <- dem %>%
  filter(!(AGE_GROUP %in% LOTT_AGE_GROUPS_NULL),
         RACE %in% LOTT_RACE,
         SEX %in% LOTT_SEX) %>%
  mutate(AGE_GROUP = fct_collapse(AGE_GROUP,
                                  "10 to 19 years"=c("10 to 14 years",
                                                     "15 to 19 years"),
                                  "20 to 29 years"=c("20 to 24 years",
                                                     "25 to 29 years"),
                                  "30 to 39 years"=c("30 to 34 years",
                                                     "35 to 39 years"),
                                  "40 to 49 years"=c("40 to 44 years",
                                                     "45 to 49 years"),
                                  "50 to 64 years"=c("50 to 54 years",
                                                     "55 to 59 years",
                                                     "60 to 64 years"),
                                  "65 years and over"=c("65 to 69 years",
                                                        "70 to 74 years",
                                                        "75 to 79 years",
                                                        "80 to 84 years",
                                                        "85 years and over"))) %>%
  mutate(AGE_GROUP = str_replace_all(AGE_GROUP," ","_")) %>%
  group_by(YEAR, STATE, RACE, SEX, AGE_GROUP) %>%
  summarise(PERC_SUB_POP = sum(PERC_SUB_POP), .groups = "drop") %>%
  unite(col = "VARIABLE", RACE, SEX, AGE_GROUP, sep = "_") %>%
  rename("VALUE"=PERC_SUB_POP)
  
dim(expand.grid(c(1:6), c(7:8), c(9:10)))[1]
```

```{r}
setequal(colnames(pop_77_79),colnames(pop_80_89))
setequal(colnames(pop_80_89),colnames(pop_90_99))
setequal(colnames(pop_90_99),colnames(pop_00_10))

head(pop_77_79)
head(pop_80_89)
head(pop_90_99)
head(pop_00_10)

population_data <- bind_rows(pop_77_79,
                             pop_80_89,
                             pop_90_99,
                             pop_00_10)

population_data %>%
  group_by(YEAR) %>%
  tally() %>%
  print(n = dim(.)[1])

population_data <- population_data %>%
  mutate(VARIABLE = "Population") %>%
  rename("VALUE"=TOT_POP)
```

## Police staffing


```{r}
colnames(ps_data)

ps_data <- ps_data %>%
  filter(data_year >= 1977, 
         data_year <= 2014) %>%
  mutate(male_total_ct = case_when(is.na(male_total_ct) ~ 0,
                                   TRUE ~ male_total_ct),
         female_total_ct = case_when(is.na(female_total_ct) ~ 0,
                                   TRUE ~ female_total_ct)) %>%
  mutate(officer_total = male_total_ct + female_total_ct) %>%
  dplyr::select(data_year,
                pub_agency_name,
                state_abbr,
                officer_total)

ps_data <- ps_data %>%
  group_by(data_year, state_abbr) %>%
  summarise(officer_state_total=sum(officer_total), .groups = "drop")

ps_data %>%
  group_by(state_abbr) %>%
  tally() %>%
  print(n = dim(.)[1])

# NB is Nebraska. This was changed to NE to avoid confusions with NB in Canada. This dataset uses NB

state_of_interest_NULL <- c("AS",
                            "GM",
                            "CZ",
                            "FS",
                            "MP",
                            "OT",
                            "PR",
                            "VI")

state_abb_df <- as.data.frame(cbind(state.abb, state.name))

colnames(state_abb_df) <- c("state_abbr", "STATE")

print(state_abb_df)

state_abb_df <- state_abb_df %>%
  add_row(state_abbr="DC",
          STATE="District of Columbia")

denominator_temp <- population_data %>%
  dplyr::select(-VARIABLE) %>%
  rename("Population_temp"=VALUE)

ps_data <- ps_data %>%
  filter(!(state_abbr %in% state_of_interest_NULL)) %>%
  mutate(state_abbr = case_when(state_abbr == "NB" ~ "NE",
                                TRUE ~ state_abbr)) %>%
  left_join(state_abb_df, by = "state_abbr") %>%
  dplyr::select(-state_abbr) %>%
  rename(YEAR = "data_year",
         VALUE = "officer_state_total") %>%
  mutate(VARIABLE = "officer_state_total") %>%
  left_join(denominator_temp, by=c("STATE","YEAR")) %>%
  mutate(VALUE = (VALUE*100000) / Population_temp) %>%
  mutate(VALUE = lag(VALUE)) %>%
  mutate(VARIABLE = "police_per_100k_lag") %>%
  dplyr::select(-Population_temp)
```

## Unemployment


```{r}


names(ue_rate_data) <- ue_rate_names

ue_rate_data$Alabama[dim(ue_rate_data$Alabama)[1],]

ue_rate_data <- ue_rate_data %>%
  map_df(bind_rows, .id = "STATE")

colnames(ue_rate_data)

sapply(ue_rate_data, class)

ue_rate_data <- ue_rate_data %>%
  mutate(Year = as.numeric(Year)) %>%
  dplyr::select(STATE, Year, Annual) %>%
  rename("YEAR"=Year,
         "VALUE"=Annual) %>%
  mutate(VARIABLE="Unemployment_rate")
```

## Poverty rate


```{r}
head(poverty_rate_data)

colnames(poverty_rate_data) <- c("STATE",
                                 "Total",
                                 "Number",
                                 "Number_se",
                                 "Percent",
                                 "Percent_se")

tail(poverty_rate_data)

notes <- 4

poverty_rate_data <- poverty_rate_data[-((dim(poverty_rate_data)[1]-notes+1):dim(poverty_rate_data)[1]),]

states_eq <- 51

extra_col <- 2

rep_rows <- states_eq + extra_col

groups <- (dim(poverty_rate_data)[1])/(rep_rows)

paste(groups - (2018-1980 + 1), "extra groups")

poverty_rate_data$year_group <- rep(1:groups, each=rep_rows)

poverty_rate_data <- poverty_rate_data %>%
  group_by(year_group) %>%
  group_split()

head(poverty_rate_data[[1]])

poverty_rate_data <- poverty_rate_data %>%
  map(~mutate(.,
              row_id = row_number())) %>%
  map(~filter(.,row_id != 2)) %>%
  map(~dplyr::select(.,-row_id))

poverty_rate_data_names <- poverty_rate_data %>%
  sapply(., "[",1,1, drop=TRUE) %>%
  str_replace_all(.,"[:space:]","_")

names(poverty_rate_data) <- poverty_rate_data_names

# Recall 2 extra groups. 
# footnotes available at https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-footnotes/cps-historic-footnotes.html

poverty_rate_data$`2017_(21)` <- NULL

poverty_rate_data$`2013_(19)` <- NULL

poverty_rate_data_names <- poverty_rate_data %>%
  sapply(., "[",1,1, drop=TRUE) %>%
  str_sub(., start = 1, end=4)

names(poverty_rate_data) <- poverty_rate_data_names

poverty_rate_data <- poverty_rate_data %>%
  map_df(bind_rows, .id = "YEAR") %>%
  dplyr::select(-year_group)

poverty_rate_data <- poverty_rate_data %>%
    mutate(n_na = rowSums(is.na(.))) 

# This shows that there is systematic missing values stemmingly *solely* from the rows without poverty data and only a label designating the year
poverty_rate_data %>% 
  group_by(n_na) %>%
  tally()

sapply(poverty_rate_data, class)

poverty_rate_data <- poverty_rate_data %>%
  drop_na() %>%
  dplyr::select(-Number,
                -Number_se,
                -Percent_se,
                -n_na,
                -Total) %>%
  rename("VALUE"=Percent) %>%
  mutate(VARIABLE = "Poverty_rate",
         YEAR = as.numeric(YEAR),
         VALUE = as.numeric(VALUE))

colnames(poverty_rate_data)
```

## Violent crime

https://www.ucrdatatool.gov/Search/Crime/State/StatebyState.cfm

```{r}
crime_data <- read_lines("docs/Crime/CrimeStatebyState.csv", skip = 2, skip_empty_rows = TRUE)

length(crime_data)

crime_data <- crime_data[-(2143:length(crime_data))]

x <- 2014-1977+1

rep_cycle <- 2 + 2 + x

rep_cycle_cut <- 2 + x

delete_rows <- c(seq(2,length(crime_data),rep_cycle),
                 seq(3,length(crime_data),rep_cycle))

crime_data <- crime_data[-delete_rows]

crime_data <- data.frame(cbind(crime_data, rep(1:(length(crime_data)/rep_cycle_cut),each=rep_cycle_cut)))

colnames(crime_data) <- c("String","STATE_GROUP")

crime_data <- crime_data %>%
  group_by(STATE_GROUP) %>%
  group_split()

columns_crime_data <- 8

crime_data <- crime_data %>%
  map(~mutate(.,
               State = case_when(str_detect(String, "Estimated crime in ") ~ substring(String, nchar("Estimated crime in ")+1)),
              row_id = row_number())) %>%
  map(~fill(., State)) %>%
  map(~filter(.,row_id > 2)) %>%
  map(~mutate(.,
              String = paste0(String, ",", State))) %>%
  map(~dplyr::select(.,String)) %>%
  map(~str_split_fixed(.$String,",",columns_crime_data + 1)) %>%
  map(~data.frame(.)) %>%
  map(~rename(.,"YEAR"=X1,
              "Extra_col1"=X2,
              "VC"=X3,
              "Extra_col2"=X4,
              "Extra_col3"=X5,
              "Extra_col4"=X6,
              "Extra_col5"=X7,
              "Extra_col6"=X8,
              "STATE"=X9)) %>%
  map(~dplyr::select(.,-contains("Extra_col"))) %>%
  map(~.x %>% mutate_all(~trimws(.,which = "both"))) %>%
  map_df(bind_rows)

sapply(crime_data, class)

crime_data <- crime_data %>%
  mutate(VARIABLE = "Viol_crime_count") %>%
  rename("VALUE" = VC) %>%
  as.tibble() %>%
  mutate(YEAR = as.numeric(YEAR),
         VALUE = as.numeric(VALUE))
```

## RTC laws


```{r}
DAWpaper_p_62 <- DAWpaper[[62]]

p_62 <- DAWpaper_p_62 %>%
    strsplit("\n") %>%
    unlist() %>%
    as.data.frame() %>%
    slice(-(1:2))

apply(p_62, 1, nchar)

p_62[53,] #physcial page 60

p_62 <- p_62 %>%
    slice(-53)

apply(p_62, 1, str_count, "\\s{5,}")
apply(p_62, 1, str_count, "\\s{10,}")
apply(p_62, 1, str_count, "\\s{20,}")
apply(p_62, 1, str_count, "\\s{40,}")

head(cbind(p_62, apply(p_62, 1, str_count, "\\s{40,}")))

p_62 <- p_62 %>%
    apply(1,str_replace_all, "\\s{40,}", "|N/A|") %>%
    str_replace_all("\\s{2,15}", "|") %>%
    as.data.frame()

p_62 <- sapply(p_62$., str_split, "\\|{1,}")

sapply(p_62, nchar)

p_62 <- lapply(p_62, function(x) x[nchar(x) > 0]) 

p_62 <- as.data.frame(do.call(rbind, p_62))

rownames(p_62)

rownames(p_62) <- c()

colnames(p_62) <- c("STATE",
                    "E_Date_RTC",
                    "Frac_Yr_Eff_Yr_Pass",
                    "RTC_Date_SA")
sapply(p_62, class)

p_62 <- p_62 %>%
  dplyr::select(STATE, RTC_Date_SA) %>%
  rename("RTC_LAW_YEAR"=RTC_Date_SA) %>%
  mutate(RTC_LAW_YEAR = as.numeric(RTC_LAW_YEAR)) %>%
  mutate(RTC_LAW_YEAR = case_when(RTC_LAW_YEAR == 0 ~ Inf,
                              TRUE ~ RTC_LAW_YEAR))

sapply(p_62, class)

head(p_62)
```

## Checkpoint

```{r}
colnames(dem_DONOHUE)
colnames(dem_LOTT)
colnames(ue_rate_data)
colnames(poverty_rate_data)
colnames(crime_data)

head(dem_DONOHUE)
head(dem_LOTT)
head(ue_rate_data)
head(poverty_rate_data)
head(crime_data)
```

## Join

## Donohue, et al.

```{r}
DONOHUE_DF <- bind_rows(dem_DONOHUE,
                        ue_rate_data,
                        poverty_rate_data,
                        crime_data,
                        population_data,
                        ps_data) %>%
  pivot_wider(names_from = "VARIABLE",
              values_from = "VALUE") %>%
  left_join(p_62 , by = c("STATE")) %>%
  mutate(RTC_LAW = case_when(YEAR >= RTC_LAW_YEAR ~ TRUE,
                              TRUE ~ FALSE))

DONOHUE_DF %>%
  group_by(YEAR) %>%
  tally() %>%
  filter(n != 51) %>%
  print(n=dim(.)[1])

summary(as.factor(DONOHUE_DF$STATE))

max(DONOHUE_DF$YEAR) - min(DONOHUE_DF$YEAR) + 1

DONOHUE_DF <- DONOHUE_DF %>%
  mutate(STATE = fct_collapse(STATE, "District of Columbia"=c("District of Columbia","D.C.")))

summary(as.factor(DONOHUE_DF$STATE))
  
length(levels(DONOHUE_DF$STATE))

DONOHUE_DF <- DONOHUE_DF %>%
  group_by(STATE, YEAR) %>%
  summarise_all(~na.omit(unique(.))) %>%
  ungroup() # This identifies unique observations, coalesces rows according to the grouping variable(s), and gets rid of of units that have incomplete data. This gives returns a dataframe with the most complete information.

summary(as.factor(DONOHUE_DF$STATE)) 

baseline_year <- min(DONOHUE_DF$YEAR)
censoring_year <- max(DONOHUE_DF$YEAR)

# Need to fix this to ensure severe bias is not introduced by prevalent "cases"

DONOHUE_DF <- DONOHUE_DF %>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

DONOHUE_DF <- DONOHUE_DF %>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))

summary(droplevels(as.factor(DONOHUE_DF$STATE)))

length(summary(droplevels(as.factor(DONOHUE_DF$STATE))))
```

## Lott and Mustard

```{r}
LOTT_DF <- bind_rows(dem_LOTT,
                     ue_rate_data,
                     poverty_rate_data,
                     crime_data,
                     population_data,
                     ps_data) %>%
  pivot_wider(names_from = "VARIABLE",
              values_from = "VALUE") %>%
  left_join(p_62 , by = c("STATE")) %>%
  mutate(RTC_LAW = case_when(YEAR >= RTC_LAW_YEAR ~ TRUE,
                              TRUE ~ FALSE))

LOTT_DF %>%
  group_by(YEAR) %>%
  tally() %>%
  filter(n != 51) %>%
  print(n=dim(.)[1])

summary(as.factor(LOTT_DF$STATE))

max(LOTT_DF$YEAR) - min(LOTT_DF$YEAR) + 1

LOTT_DF <- LOTT_DF %>%
  mutate(STATE = fct_collapse(STATE, "District of Columbia"=c("District of Columbia","D.C.")))

summary(as.factor(LOTT_DF$STATE))
  
length(levels(LOTT_DF$STATE))

LOTT_DF <- LOTT_DF %>%
  group_by(STATE, YEAR) %>%
  summarise_all(~na.omit(unique(.))) %>%
  ungroup() # This identifies unique observations, coalesces rows according to the grouping variable(s), and gets rid of of units that have incomplete data. This gives returns a dataframe with the most complete information.

summary(as.factor(LOTT_DF$STATE)) 

baseline_year <- min(LOTT_DF$YEAR)
censoring_year <- max(LOTT_DF$YEAR)

# Need to fix this to ensure severe bias is not introduced by prevalent "cases"

LOTT_DF <- LOTT_DF %>%
  mutate(TIME_0 = baseline_year,
         TIME_INF = censoring_year) %>%
  filter(RTC_LAW_YEAR > TIME_0)

LOTT_DF <- LOTT_DF %>%
  mutate(Viol_crime_rate_1k = (Viol_crime_count*1000)/Population,
         Viol_crime_rate_1k_log = log(Viol_crime_rate_1k),
         Population_log = log(Population))

summary(droplevels(as.factor(LOTT_DF$STATE)))

length(summary(droplevels(as.factor(LOTT_DF$STATE))))
```

# **Data Exploration**
***

```{r}
sapply(DONOHUE_DF, class)

DONOHUE_DF %>%
  mutate(Viol_crime_rate_100k_log = log((Viol_crime_count*100000)/Population)) %>%
  ggplot(aes(x = YEAR, y = Viol_crime_rate_100k_log, color = STATE)) +
  geom_point(size = 0.5) +
  geom_line(aes(group=STATE),
            size = 0.5,
            show.legend = FALSE) +
  geom_text_repel(data = DONOHUE_DF %>%
              mutate(Viol_crime_rate_100k_log = log((Viol_crime_count*100000)/Population)) %>%
              filter(YEAR == last(YEAR)),
            aes(label = STATE,
                x = YEAR,
                y = Viol_crime_rate_100k_log),
            size = 3,
            alpha = 1,
            nudge_x = 10,
            direction = "y",
            hjust = 1,
            vjust = 1,
            segment.size = 0.25,
            segment.alpha = 0.25,
            force = 1,
            max.iter = 9999) +
  guides(color = FALSE) +
  scale_x_continuous(breaks = seq(1980, 2015, by = 1),
                     limits = c(1980, 2015),
                     labels = c(seq(1980, 2010, by = 1), rep("", 5))) +
  scale_y_continuous(breaks = seq(3.5, 8.5, by = 0.5),
                     limits = c(3.5, 8.5)) +
  labs(title = "States have different levels of crime",
       x = "Year",
       y = "ln(violent crimes per 100,000 people)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90))

DONOHUE_DF %>%
  group_by(YEAR) %>%
  summarise(Viol_crime_count = sum(Viol_crime_count),
            Population = sum(Population),
            .groups = "drop") %>%
  mutate(Viol_crime_rate_100k_log = log((Viol_crime_count*100000)/Population)) %>%
  ggplot(aes(x = YEAR, y = Viol_crime_rate_100k_log)) +
  geom_line() +
  scale_x_continuous(breaks = seq(1980, 2010, by = 1),
                     limits = c(1980, 2010),
                     labels = c(seq(1980, 2010, by = 1))) +
  scale_y_continuous(breaks = seq(5.75, 6.75, by = 0.25),
                     limits = c(5.75, 6.75)) +
  labs(title = "Crime rates fluctuate over time",
       x = "Year",
       y = "ln(violent crimes per 100,000 people)") +
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 90))
```

# **Data Analysis**
***

## Donohue, et al.

Some code taken from http://karthur.org/2019/implementing-fixed-effects-panel-models-in-r.html

```{r}
d_panel_DONOHUE <- pdata.frame(DONOHUE_DF, index=c("STATE", "YEAR"))

DONOHUE_OUTPUT <- plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
                      effect = "twoways",
                      model = "within",
                      data=d_panel_DONOHUE)

summary(DONOHUE_OUTPUT)

DONOHUE_OUTPUT_TIDY <- tidy(DONOHUE_OUTPUT, conf.int = 0.95)

DONOHUE_OUTPUT_TIDY$Analysis <- "Analysis 1"
```

## Lott and Mustard

Some code taken from http://karthur.org/2019/implementing-fixed-effects-panel-models-in-r.html

```{r}
LOTT_variables <- LOTT_DF %>%
  dplyr::select(RTC_LAW,
                contains(c("White","Black","Other")),
                Unemployment_rate,
                Poverty_rate,
                Population_log,
                police_per_100k_lag) %>%
  colnames()

LOTT_fmla <- as.formula(paste("Viol_crime_rate_1k_log ~",
                              paste(LOTT_variables, collapse = " + ")
                              )
                        )

d_panel_LOTT <- pdata.frame(LOTT_DF, index=c("STATE", "YEAR"))

LOTT_OUTPUT <- plm(LOTT_fmla,
                      model = "within",
                   effect = "twoways",
                      data=d_panel_LOTT)

summary(LOTT_OUTPUT)

LOTT_OUTPUT_TIDY <- tidy(LOTT_OUTPUT, conf.int = 0.95)

LOTT_OUTPUT_TIDY$Analysis <- "Analysis 2"
```

## Comparing analyses

```{r}
comparing_analyses <- DONOHUE_OUTPUT_TIDY %>%
  bind_rows(LOTT_OUTPUT_TIDY) %>%
  filter(term == "RTC_LAWTRUE")

library(grid)

comparing_analyses_plot <- ggplot(comparing_analyses) + 
  geom_point(aes(x = Analysis, y = estimate)) +
  geom_errorbar(aes(x = Analysis, ymin = conf.low, ymax = conf.high), width = 0.25) + 
  geom_hline(yintercept = 0, color = "red") +
  scale_y_continuous(breaks = seq(-0.2, 0.2, by = 0.05),
                     labels = seq(-0.2, 0.2, by = 0.05),
                     limits = c(-0.2,0.2)) +
  geom_segment(aes(x = 1, y = 0.125, xend = 1, yend = 0.175),
               arrow = arrow(angle = 45, ends = "last", type = "open"),
               size = 2,
               color = "green",
               lineend = "butt",
               linejoin = "mitre") +
  geom_segment(aes(x = 2, y = -0.125, xend = 2, yend = -0.175),
               arrow = arrow(angle = 45, ends = "last", type = "open"),
               size = 2,
               color = "red",
               lineend = "butt",
               linejoin = "mitre") +
  theme_minimal() + 
  theme(axis.title.x = element_blank(),
        axis.text = element_text(size = 12)) +
  labs(title = "Effect estimate on ln(violent crimes per 100,000 people)",
       y = "Effect estimate (95% CI)")

comparing_analyses_plot
```

# Multicollinearity analysis

How did the above happen?

The analysis dataframes are very similar yet rendered very different results. 

```{r}
all_equal(target = DONOHUE_DF,
          current = LOTT_DF,
          ignore_col_order = TRUE,
          ignore_row_order = TRUE)

dim(DONOHUE_DF)[1] == dim(LOTT_DF)[1]
```

The only difference between the two dataframes rests in how the demographic variables were parameterized.

```{r}
DONOHUE_DF %>%
  dplyr::select(contains("years")) %>%
  colnames()

LOTT_DF %>%
  dplyr::select(contains("years")) %>%
  colnames()
```

Clearly, this had an effect on the results of the analysis. 

Let's explore how this occured. 

When seemingly independent variables are highly related to one another, the relationships estimated in an analysis may be distorted. 

In regression analysis, this distortion is often a byproduct of a violation of the independence assumption. This distortion, if large enough, can impact statistical inference. 

There are several ways we can diagnose multicollinearity.

### Correlation

Again, multicollinearity often occurs when independent variables are highly related to one another. Consequently, we can evaluate these relationships be examining the correlation between variable pairs.

<style>
div.blue { background-color:#e6f0ff; border-radius: 5px; padding: 20px;}
</style>
<div class = "blue">

It is important to note that multicollinearity and correlation are not one and the same. Correlation can be thought of as the strength of the relationship between variables. On the other hand, multicollinearity can be thought of the the violation of the independence assumption that is a consequence of this correlation in a regression analysis. 

</div>

#### Scatterplots

```{r}
colnames(DONOHUE_DF)

DONOHUE_DF %>% 
  dplyr::select(RTC_LAW,
                Viol_crime_rate_1k_log,
                Unemployment_rate,
                Poverty_rate,
                Population_log) %>% 
  ggpairs(.,
          columns = c(2:5),
          lower = list(continuous = wrap("smooth_loess",
                                         color = "red",
                                         alpha = 0.5,
                                         size = 0.1)))

LOTT_DF %>% 
  dplyr::select(RTC_LAW,
                Viol_crime_rate_1k_log,
                Unemployment_rate,
                Poverty_rate,
                Population_log) %>% 
  ggpairs(.,
          columns = c(2:5),
          lower = list(continuous = wrap("smooth_loess",
                                         color = "red",
                                         alpha = 0.5,
                                         size = 0.1)))
```

#### Heatmaps

```{r}
cor_DONOHUE_dem <- cor(DONOHUE_DF %>% dplyr::select(contains("_years")))

corr_mat_DONOHUE <- ggcorrplot(cor_DONOHUE_dem,
           tl.cex = 6,
           hc.order = TRUE,
           colors = c("red",
                      "white",
                      "red"),
           outline.color = "transparent",
           title = "Correlation Matrix, Analysis 1",
           legend.title = TeX("$\\rho$"))

corr_mat_DONOHUE

cor_LOTT_dem <- cor(LOTT_DF %>% dplyr::select(contains("_years")))

corr_mat_LOTT <- ggcorrplot(cor_LOTT_dem,
           tl.cex = 6,
           hc.order = TRUE,
           colors = c("red",
                      "white",
                      "red"),
           outline.color = "transparent",
           title = "Correlation Matrix, Analysis 2",
           legend.title = TeX("$\\rho$"))

corr_mat_LOTT
```

### Coefficient estimate instability

```{r}
sims <- 250

# DONOHUE

# round(dim(DONOHUE_DF)[1]/2)
samps_DONOHUE <- lapply(rep(dim(DONOHUE_DF)[1]-1, sims),
       function(x)DONOHUE_DF[sample(nrow(DONOHUE_DF),
                                     size = x, replace = FALSE),])

fit_nls_on_bootstrap_DONOHUE <- function(split){
  plm(Viol_crime_rate_1k_log ~
                        RTC_LAW +
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log + 
                        police_per_100k_lag,
      data = data.frame(split),
      index = c("STATE","YEAR"),
      model = "within",
      effect = "twoways")
}
  
samps_models_DONOHUE <- lapply(samps_DONOHUE, fit_nls_on_bootstrap_DONOHUE)

samps_models_DONOHUE <- samps_models_DONOHUE %>%
  map(tidy)

names(samps_models_DONOHUE) <- paste0("DONOHUE_",1:length(samps_models_DONOHUE))

simulations_DONOHUE <- samps_models_DONOHUE %>%
  bind_rows(.id = "ID") %>%
  mutate(Analysis = "Analysis 1")

## LOTT

samps_LOTT <- lapply(rep(round(dim(LOTT_DF)[1]/2), sims),
       function(x) LOTT_DF[sample(nrow(LOTT_DF),
                                  size = x, replace = FALSE),])

fit_nls_on_bootstrap_LOTT <- function(split){
  plm(LOTT_fmla,
      data = data.frame(split),
      index = c("STATE","YEAR"),
      model = "within",
      effect = "twoways")
}
  
samps_models_LOTT <- lapply(samps_LOTT, fit_nls_on_bootstrap_LOTT)

samps_models_LOTT <- samps_models_LOTT %>%
  map(tidy)

names(samps_models_LOTT) <- paste0("LOTT_",1:length(samps_models_LOTT))

simulations_LOTT <- samps_models_LOTT %>%
  bind_rows(.id = "Analysis") %>%
  mutate(Analysis = "Analysis 2")

simulations <- bind_rows(simulations_DONOHUE,
                         simulations_LOTT)

simulation_plot <- simulations %>%
  filter(term=="RTC_LAWTRUE") %>%
  ggplot(aes(x = Analysis, y = estimate)) + 
  geom_jitter(alpha = 0.25,
              width = 0.1) + 
  labs(title = "Coefficient instability",
       subtitle = "Estimates sensitive to observation deletions",
       x = "Term",
       y = "Coefficient",
       caption = "Results from simulations") + 
  theme_minimal() +
  theme(axis.title.x = element_blank())

simulation_plot
```

### VIF

```{r}
design.matrix <- as.data.frame(model.matrix(DONOHUE_OUTPUT))

design.matrix$Viol_crime_rate_1k_log <- plm::Within(
  d_panel_DONOHUE$Viol_crime_rate_1k_log)

lm_DONOHUE <- lm(Viol_crime_rate_1k_log ~
                        RTC_LAWTRUE + # logical class changes variable name after inital model
                        White_Male_15_to_19_years +
                        White_Male_20_to_39_years +
                        Black_Male_15_to_19_years +
                        Black_Male_20_to_39_years +
                        Other_Male_15_to_19_years +
                        Other_Male_20_to_39_years +
                        Unemployment_rate +
                        Poverty_rate + 
                        Population_log +
               police_per_100k_lag,
             data = design.matrix)


vif(lm_DONOHUE)

vif_DONOHUE <- vif(lm_DONOHUE)

vif_DONOHUE <- vif_DONOHUE %>%
  as_tibble() %>%
  cbind(., names(vif_DONOHUE)) %>%
  as_tibble()
  
colnames(vif_DONOHUE) <- c("VIF", "Variable")

max_vif_DONOHUE <- max(vif(lm_DONOHUE)) 
```

```{r}
design.matrix <- as.data.frame(model.matrix(LOTT_OUTPUT))

design.matrix$Viol_crime_rate_1k_log <- plm::Within(
  d_panel_LOTT$Viol_crime_rate_1k_log)

LOTT_variables_ols <- LOTT_DF %>%
  dplyr::select(RTC_LAW,
                contains(c("White","Black","Other")),
                Unemployment_rate,
                Poverty_rate,
                Population_log,
                police_per_100k_lag) %>%
  colnames() %>%
  str_replace("RTC_LAW", "RTC_LAWTRUE") # logical class changes variable name after inital model

LOTT_fmla_ols <- as.formula(paste("Viol_crime_rate_1k_log ~",
                              paste(LOTT_variables_ols, collapse = " + ")
                              )
                        )

lm_LOTT <- lm(LOTT_fmla_ols,
             data = design.matrix)

vif(lm_LOTT)

vif_LOTT <- vif(lm_LOTT)

vif_LOTT <- vif_LOTT %>%
  as_tibble() %>%
  cbind(., names(vif_LOTT)) %>%
  as_tibble()
  
colnames(vif_LOTT) <- c("VIF", "Variable")

max_vif_LOTT <- max(vif(lm_LOTT))
```

```{r, echo=FALSE}
#This could be used to label the max VIF of each analysis

max_vif_DONOHUE
max_vif_LOTT
```

$$\frac{1}{1-R_{i}^{2}}$$

```{r}
vif_DONOHUE$Analysis <- "Analysis 1"
vif_LOTT$Analysis <- "Analysis 2"

vif_df <- rbind(vif_DONOHUE,
                vif_LOTT)

vif_plot <- vif_df %>%
  ggplot(aes(x = Analysis, y = VIF)) +
  geom_jitter(width = 0.1, alpha = 0.5, size = 2) +
  geom_hline(yintercept = 10, color = "red") +
  scale_y_continuous(trans = 'log10',
                     limits = c(1,1000)) +
  labs(title = "Variance inflation factors") + 
  theme_minimal() +
  theme(axis.title.x = element_blank())

vif_plot
```

# Synthesis

```{r, fig.height=10, echo=FALSE, message=FALSE, warning=FALSE}
title_plots <- ggdraw() + 
  draw_label(
    "Multicollinearity and its effects",
    fontface = 'bold',
    size=18,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

forward <- ggdraw() + 
  draw_label(
    "Analysis 1: 6 demographic variables\nAnalysis 2: 36 demographic variables",
    fontface = 'bold',
    size=10,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

corr_mat_DONOHUE <- ggcorrplot(cor_DONOHUE_dem,
                               tl.cex = 6,
                               hc.order = TRUE,
                               outline.color = "transparent",
                               colors = c("red",
                                          "white",
                                          "red"),
                            legend.title = TeX("$\\rho$")) +
  theme_void() + 
  theme(plot.title= element_text(size=8)) +
  labs(title = "Analysis 1") 

corr_mat_LOTT <- ggcorrplot(cor_LOTT_dem,
                            tl.cex = 6,
                            hc.order = TRUE,
                            outline.color = "transparent",
                            colors = c("red",
                                       "white",
                                       "red"),
                            legend.title = TeX("$\\rho$")) +
  theme_void() + 
  theme(plot.title = element_text(size=8)) +
  labs(title = "Analysis 2") 

plot_A1 <- corr_mat_DONOHUE

plot_A2 <- corr_mat_LOTT

row_A <- plot_grid(plot_A1,
                   plot_A2,
                   nrow = 1)

title_A <- ggdraw() + 
  draw_label(
    "Correlation between variables can induce multicollinearity",
    fontface = 'bold',
    size=14,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

legend_A <- get_legend(corr_mat_LOTT)

plot_A <- plot_grid(title_A,
                    row_A,
                    ncol = 1,
                    rel_heights = c(0.1,1))

empty_df <- cbind(c(1:10),c(1:10)) %>%
  as.data.frame()

colnames(empty_df) <- c("X", "Y")

plot_B1 <- ggplot(empty_df, aes(x = X, y = Y)) +
  annotate("text",
           x=5,
           y=5,
           label = TeX("$VIF_{i} = \\frac{1}{1-R_{i}^{2}}$"),
           size = 8) +
  theme_void()

plot_B2 <- vif_plot +
  theme(axis.title.x = element_text(size=8))

row_B <- plot_grid(plot_B1,
                       plot_B2,
                       nrow = 1)

title_B <- ggdraw() + 
  draw_label(
    "Variance inflation factors can be used to identify multicollinearity when present",
    fontface = 'bold',
    size=14,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plot_B <- plot_grid(title_B,
                    row_B,
                    ncol = 1,
                    rel_heights = c(0.1,1))

plot_C1 <- comparing_analyses_plot + 
  theme(axis.text.x = element_text(size = 8),
        axis.title.x = element_blank()) +
  labs(title = "Introduces bias to estimates",
       subtitle = "Bias introduced can change direction of estimate")

plot_C2 <- simulation_plot +
  labs(title = "Reduces precision in estimates")

row_C <- plot_grid(plot_C1,
                       plot_C2,
                       nrow = 1)

title_C <- ggdraw() + 
  draw_label(
    "Multicollinearity can have a negative effect on statistical inference",
    fontface = 'bold',
    size=14,
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

plot_C <- plot_grid(title_C,
                    row_C,
                    ncol = 1,
                    rel_heights = c(0.1,1))

plots <- plot_grid(plot_A,
                   plot_B,
                   plot_C,
          ncol = 1,
          rel_heights = c(1,1,1))

mainplot <- plot_grid(title_plots,
                       forward,
                       plots,
                       #legend_uw,
                       ncol = 1,
                       rel_heights = c(0.05,
                                       0.05,
                                       1))

mainplot
```



```{r, echo=FALSE, include=FALSE}
ggsave(here::here("img", "mainplot.png"))
```




# **Data Visualization**
*** 

# **Summary**
*** 

# **Suggested Homework**
*** 

# **Helpful Links**
*** 

https://rpubs.com/rslbliss/fixed_effects

http://karthur.org/2019/implementing-fixed-effects-panel-models-in-r.html

https://stats.stackexchange.com/questions/99236/effects-in-panel-models-individual-time-or-twoways

